library(ggplot2)
library(tidyverse)
## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.2 ✔ readr 2.1.4
## ✔ forcats 1.0.0 ✔ stringr 1.5.0
## ✔ lubridate 1.9.2 ✔ tibble 3.2.1
## ✔ purrr 1.0.2 ✔ tidyr 1.3.0
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
library(dplyr)
library(meta)
## Loading 'meta' package (version 6.5-0).
## Type 'help(meta)' for a brief overview.
## Readers of 'Meta-Analysis with R (Use R!)' should install
## older version of 'meta' package: https://tinyurl.com/dt4y5drs
library(PRISMAstatement)
library(skimr)
library(MASS)
##
## Attaching package: 'MASS'
##
## The following object is masked from 'package:dplyr':
##
## select
library(ggpubr)
setwd("~/Desktop/Chapter 4")
photo <- read.csv("observations_2022.csv")
summary(photo)
## region site site_code microsite
## Length:58015 Length:58015 Length:58015 Length:58015
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
## plot cam_ID month day
## Min. :1.000 Min. :1.000 Length:58015 Min. : 3.00
## 1st Qu.:1.000 1st Qu.:1.000 Class :character 1st Qu.: 6.00
## Median :1.000 Median :2.000 Mode :character Median :16.00
## Mean :1.775 Mean :1.527 Mean :13.98
## 3rd Qu.:2.000 3rd Qu.:2.000 3rd Qu.:22.00
## Max. :4.000 Max. :2.000 Max. :29.00
## year shrub_density rep identified_by
## Min. :2022 Min. : 0.000 Min. : 1 Length:58015
## 1st Qu.:2022 1st Qu.: 0.000 1st Qu.: 5386 Class :character
## Median :2022 Median : 0.000 Median :12638 Mode :character
## Mean :2022 Mean : 4.681 Mean :15170
## 3rd Qu.:2022 3rd Qu.:11.000 3rd Qu.:24318
## Max. :2022 Max. :14.000 Max. :38822
## filename timestamp animal.hit class
## Length:58015 Length:58015 Min. :0.00000 Length:58015
## Class :character Class :character 1st Qu.:0.00000 Class :character
## Mode :character Mode :character Median :0.00000 Mode :character
## Mean :0.06521
## 3rd Qu.:0.00000
## Max. :1.00000
## order family genus species
## Length:58015 Length:58015 Length:58015 Length:58015
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
## common_name number_of_objects
## Length:58015 Min. : 1.000
## Class :character 1st Qu.: 1.000
## Mode :character Median : 1.000
## Mean : 1.001
## 3rd Qu.: 1.000
## Max. :12.000
photo <- photo %>%
filter(common_name != "Human")
photo <- photo %>%
filter(common_name != "Human-Camera Trapper")
photo <- photo %>%
filter(common_name != "Domestic Dog")
photo <- photo %>%
filter(common_name != "Vehicle")
photo <- photo %>%
dplyr::filter(common_name != "Insect")
photo <- photo %>%
dplyr::filter(common_name != "Animal")
photo <- photo %>%
dplyr::filter(common_name != "Bird")
photo <- photo %>%
filter(common_name != "No CV Result")
count.hit <- photo %>%
count(animal.hit) %>%
na.omit()
summary(count.hit)
## animal.hit n
## Min. :0.00 Min. : 3169
## 1st Qu.:0.25 1st Qu.:15935
## Median :0.50 Median :28700
## Mean :0.50 Mean :28700
## 3rd Qu.:0.75 3rd Qu.:41466
## Max. :1.00 Max. :54232
### 2022 Had a 5.88% catch rate
### Animal Observations by Site_Code
animals_by_sitecode <- photo%>%
group_by(site_code, microsite, common_name) %>%
summarise(captures = sum(animal.hit), n = n())
## `summarise()` has grouped output by 'site_code', 'microsite'. You can override
## using the `.groups` argument.
animals_by_sitecode <- animals_by_sitecode %>%
filter(common_name != "Blank")
### Animal observations by Site
animals_by_site <- photo %>% group_by(site,microsite,common_name) %>% summarise(captures = sum(animal.hit))
## `summarise()` has grouped output by 'site', 'microsite'. You can override using
## the `.groups` argument.
animals_by_site <- animals_by_site %>% filter(common_name != "Blank")
### Animal observations by Density
animals_by_density <- photo %>% group_by(microsite,common_name) %>% summarise(captures = sum(animal.hit))
## `summarise()` has grouped output by 'microsite'. You can override using the
## `.groups` argument.
animals_by_density <- animals_by_density %>% filter(common_name != "Blank") %>% filter(common_name != "No CV Result")
### Total Observations 2022
Total_Observations <- photo %>% group_by(common_name) %>% summarise(total = sum(animal.hit)) %>% filter(common_name != "Blank") %>% filter(common_name != "No CV Result")
density_obvs <- merge(animals_by_density, Total_Observations, all = TRUE)
density_obvs$percent_presence <- density_obvs$captures/density_obvs$total
### Percent proportion Figure
plot1 <- ggplot(density_obvs, aes(common_name, percent_presence, fill = microsite)) + geom_bar(stat = "identity") + coord_flip() + theme_classic() + scale_x_discrete(limits=rev) + xlab("Species") + ylab("Percent Proportion") + labs(fill = "Microsite")
plot1 + scale_fill_manual(values = c("#009900", "#0066cc"))
library(emmeans)
m1 <- glm(total ~ microsite*common_name, family = "poisson", data = density_obvs)
anova(m1, test = "Chisq")
## Analysis of Deviance Table
##
## Model: poisson, link: log
##
## Response: total
##
## Terms added sequentially (first to last)
##
##
## Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL 44 22806
## microsite 1 5.4 43 22801 0.01984 *
## common_name 26 22800.9 17 0 < 2e-16 ***
## microsite:common_name 17 0.0 0 0 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
e1 <- emmeans(m1, pairwise~common_name)
## NOTE: Results may be misleading due to involvement in interactions
e1
## $emmeans
## common_name emmean SE df asymp.LCL asymp.UCL
## American Robin nonEst NA NA NA NA
## Black-tailed Jackrabbit 5.442 0.04652 Inf 5.3512 5.534
## Blunt-nosed Leopard Lizard 1.099 0.40825 Inf 0.2985 1.899
## Bobcat 1.792 0.28868 Inf 1.2260 2.358
## Brewer's Blackbird 2.485 0.20412 Inf 2.0848 2.885
## California Ground Squirrel 5.442 0.04652 Inf 5.3512 5.534
## California Pocket Mouse 1.386 0.35355 Inf 0.6933 2.079
## California Quail 3.178 0.14434 Inf 2.8952 3.461
## California Thrasher nonEst NA NA NA NA
## Common Raven 3.989 0.09623 Inf 3.8004 4.178
## Coyote 4.489 0.07495 Inf 4.3417 4.636
## Desert Cottontail 4.078 0.09206 Inf 3.8971 4.258
## Desert Iguana nonEst NA NA NA NA
## Giant Kangaroo Rat 4.078 0.09206 Inf 3.8971 4.258
## Great White Egret nonEst NA NA NA NA
## Greater Roadrunner 1.946 0.26726 Inf 1.4221 2.470
## Heermann's Kangaroo Rat 7.680 0.01520 Inf 7.6504 7.710
## Killdeer nonEst NA NA NA NA
## Kit Fox 2.079 0.25000 Inf 1.5895 2.569
## Lark Sparrow 1.946 0.26726 Inf 1.4221 2.470
## Loggerhead Shrike 2.079 0.25000 Inf 1.5895 2.569
## Mohave Ground Squirrel nonEst NA NA NA NA
## Mourning Dove 1.609 0.31623 Inf 0.9896 2.229
## Nelson's Antelope Squirrel 4.956 0.05934 Inf 4.8395 5.072
## Red-tailed Hawk nonEst NA NA NA NA
## Salinas Pocket Mouse nonEst NA NA NA NA
## Vesper Sparrow nonEst NA NA NA NA
##
## Results are averaged over the levels of: microsite
## Results are given on the log (not the response) scale.
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df
## American Robin - (Black-tailed Jackrabbit) nonEst NA NA
## American Robin - (Blunt-nosed Leopard Lizard) nonEst NA NA
## American Robin - Bobcat nonEst NA NA
## American Robin - Brewer's Blackbird nonEst NA NA
## American Robin - California Ground Squirrel nonEst NA NA
## American Robin - California Pocket Mouse nonEst NA NA
## American Robin - California Quail nonEst NA NA
## American Robin - California Thrasher nonEst NA NA
## American Robin - Common Raven nonEst NA NA
## American Robin - Coyote nonEst NA NA
## American Robin - Desert Cottontail nonEst NA NA
## American Robin - Desert Iguana nonEst NA NA
## American Robin - Giant Kangaroo Rat nonEst NA NA
## American Robin - Great White Egret nonEst NA NA
## American Robin - Greater Roadrunner nonEst NA NA
## American Robin - Heermann's Kangaroo Rat nonEst NA NA
## American Robin - Killdeer nonEst NA NA
## American Robin - Kit Fox nonEst NA NA
## American Robin - Lark Sparrow nonEst NA NA
## American Robin - Loggerhead Shrike nonEst NA NA
## American Robin - Mohave Ground Squirrel nonEst NA NA
## American Robin - Mourning Dove nonEst NA NA
## American Robin - Nelson's Antelope Squirrel nonEst NA NA
## American Robin - (Red-tailed Hawk) nonEst NA NA
## American Robin - Salinas Pocket Mouse nonEst NA NA
## American Robin - Vesper Sparrow nonEst NA NA
## (Black-tailed Jackrabbit) - (Blunt-nosed Leopard Lizard) 4.3438 0.4109 Inf
## (Black-tailed Jackrabbit) - Bobcat 3.6507 0.2924 Inf
## (Black-tailed Jackrabbit) - Brewer's Blackbird 2.9575 0.2094 Inf
## (Black-tailed Jackrabbit) - California Ground Squirrel 0.0000 0.0658 Inf
## (Black-tailed Jackrabbit) - California Pocket Mouse 4.0561 0.3566 Inf
## (Black-tailed Jackrabbit) - California Quail 2.2644 0.1517 Inf
## (Black-tailed Jackrabbit) - California Thrasher nonEst NA NA
## (Black-tailed Jackrabbit) - Common Raven 1.4534 0.1069 Inf
## (Black-tailed Jackrabbit) - Coyote 0.9538 0.0882 Inf
## (Black-tailed Jackrabbit) - Desert Cottontail 1.3649 0.1031 Inf
## (Black-tailed Jackrabbit) - Desert Iguana nonEst NA NA
## (Black-tailed Jackrabbit) - Giant Kangaroo Rat 1.3649 0.1031 Inf
## (Black-tailed Jackrabbit) - Great White Egret nonEst NA NA
## (Black-tailed Jackrabbit) - Greater Roadrunner 3.4965 0.2713 Inf
## (Black-tailed Jackrabbit) - Heermann's Kangaroo Rat -2.2378 0.0489 Inf
## (Black-tailed Jackrabbit) - Killdeer nonEst NA NA
## (Black-tailed Jackrabbit) - Kit Fox 3.3630 0.2543 Inf
## (Black-tailed Jackrabbit) - Lark Sparrow 3.4965 0.2713 Inf
## (Black-tailed Jackrabbit) - Loggerhead Shrike 3.3630 0.2543 Inf
## (Black-tailed Jackrabbit) - Mohave Ground Squirrel nonEst NA NA
## (Black-tailed Jackrabbit) - Mourning Dove 3.8330 0.3196 Inf
## (Black-tailed Jackrabbit) - Nelson's Antelope Squirrel 0.4866 0.0754 Inf
## (Black-tailed Jackrabbit) - (Red-tailed Hawk) nonEst NA NA
## (Black-tailed Jackrabbit) - Salinas Pocket Mouse nonEst NA NA
## (Black-tailed Jackrabbit) - Vesper Sparrow nonEst NA NA
## (Blunt-nosed Leopard Lizard) - Bobcat -0.6931 0.5000 Inf
## (Blunt-nosed Leopard Lizard) - Brewer's Blackbird -1.3863 0.4564 Inf
## (Blunt-nosed Leopard Lizard) - California Ground Squirrel -4.3438 0.4109 Inf
## (Blunt-nosed Leopard Lizard) - California Pocket Mouse -0.2877 0.5401 Inf
## (Blunt-nosed Leopard Lizard) - California Quail -2.0794 0.4330 Inf
## (Blunt-nosed Leopard Lizard) - California Thrasher nonEst NA NA
## (Blunt-nosed Leopard Lizard) - Common Raven -2.8904 0.4194 Inf
## (Blunt-nosed Leopard Lizard) - Coyote -3.3900 0.4151 Inf
## (Blunt-nosed Leopard Lizard) - Desert Cottontail -2.9789 0.4185 Inf
## (Blunt-nosed Leopard Lizard) - Desert Iguana nonEst NA NA
## (Blunt-nosed Leopard Lizard) - Giant Kangaroo Rat -2.9789 0.4185 Inf
## (Blunt-nosed Leopard Lizard) - Great White Egret nonEst NA NA
## (Blunt-nosed Leopard Lizard) - Greater Roadrunner -0.8473 0.4879 Inf
## (Blunt-nosed Leopard Lizard) - Heermann's Kangaroo Rat -6.5816 0.4085 Inf
## (Blunt-nosed Leopard Lizard) - Killdeer nonEst NA NA
## (Blunt-nosed Leopard Lizard) - Kit Fox -0.9808 0.4787 Inf
## (Blunt-nosed Leopard Lizard) - Lark Sparrow -0.8473 0.4879 Inf
## (Blunt-nosed Leopard Lizard) - Loggerhead Shrike -0.9808 0.4787 Inf
## (Blunt-nosed Leopard Lizard) - Mohave Ground Squirrel nonEst NA NA
## (Blunt-nosed Leopard Lizard) - Mourning Dove -0.5108 0.5164 Inf
## (Blunt-nosed Leopard Lizard) - Nelson's Antelope Squirrel -3.8572 0.4125 Inf
## (Blunt-nosed Leopard Lizard) - (Red-tailed Hawk) nonEst NA NA
## (Blunt-nosed Leopard Lizard) - Salinas Pocket Mouse nonEst NA NA
## (Blunt-nosed Leopard Lizard) - Vesper Sparrow nonEst NA NA
## Bobcat - Brewer's Blackbird -0.6931 0.3536 Inf
## Bobcat - California Ground Squirrel -3.6507 0.2924 Inf
## Bobcat - California Pocket Mouse 0.4055 0.4564 Inf
## Bobcat - California Quail -1.3863 0.3227 Inf
## Bobcat - California Thrasher nonEst NA NA
## Bobcat - Common Raven -2.1972 0.3043 Inf
## Bobcat - Coyote -2.6969 0.2982 Inf
## Bobcat - Desert Cottontail -2.2858 0.3030 Inf
## Bobcat - Desert Iguana nonEst NA NA
## Bobcat - Giant Kangaroo Rat -2.2858 0.3030 Inf
## Bobcat - Great White Egret nonEst NA NA
## Bobcat - Greater Roadrunner -0.1542 0.3934 Inf
## Bobcat - Heermann's Kangaroo Rat -5.8884 0.2891 Inf
## Bobcat - Killdeer nonEst NA NA
## Bobcat - Kit Fox -0.2877 0.3819 Inf
## Bobcat - Lark Sparrow -0.1542 0.3934 Inf
## Bobcat - Loggerhead Shrike -0.2877 0.3819 Inf
## Bobcat - Mohave Ground Squirrel nonEst NA NA
## Bobcat - Mourning Dove 0.1823 0.4282 Inf
## Bobcat - Nelson's Antelope Squirrel -3.1641 0.2947 Inf
## Bobcat - (Red-tailed Hawk) nonEst NA NA
## Bobcat - Salinas Pocket Mouse nonEst NA NA
## Bobcat - Vesper Sparrow nonEst NA NA
## Brewer's Blackbird - California Ground Squirrel -2.9575 0.2094 Inf
## Brewer's Blackbird - California Pocket Mouse 1.0986 0.4082 Inf
## Brewer's Blackbird - California Quail -0.6931 0.2500 Inf
## Brewer's Blackbird - California Thrasher nonEst NA NA
## Brewer's Blackbird - Common Raven -1.5041 0.2257 Inf
## Brewer's Blackbird - Coyote -2.0037 0.2175 Inf
## Brewer's Blackbird - Desert Cottontail -1.5926 0.2239 Inf
## Brewer's Blackbird - Desert Iguana nonEst NA NA
## Brewer's Blackbird - Giant Kangaroo Rat -1.5926 0.2239 Inf
## Brewer's Blackbird - Great White Egret nonEst NA NA
## Brewer's Blackbird - Greater Roadrunner 0.5390 0.3363 Inf
## Brewer's Blackbird - Heermann's Kangaroo Rat -5.1953 0.2047 Inf
## Brewer's Blackbird - Killdeer nonEst NA NA
## Brewer's Blackbird - Kit Fox 0.4055 0.3227 Inf
## Brewer's Blackbird - Lark Sparrow 0.5390 0.3363 Inf
## Brewer's Blackbird - Loggerhead Shrike 0.4055 0.3227 Inf
## Brewer's Blackbird - Mohave Ground Squirrel nonEst NA NA
## Brewer's Blackbird - Mourning Dove 0.8755 0.3764 Inf
## Brewer's Blackbird - Nelson's Antelope Squirrel -2.4709 0.2126 Inf
## Brewer's Blackbird - (Red-tailed Hawk) nonEst NA NA
## Brewer's Blackbird - Salinas Pocket Mouse nonEst NA NA
## Brewer's Blackbird - Vesper Sparrow nonEst NA NA
## California Ground Squirrel - California Pocket Mouse 4.0561 0.3566 Inf
## California Ground Squirrel - California Quail 2.2644 0.1517 Inf
## California Ground Squirrel - California Thrasher nonEst NA NA
## California Ground Squirrel - Common Raven 1.4534 0.1069 Inf
## California Ground Squirrel - Coyote 0.9538 0.0882 Inf
## California Ground Squirrel - Desert Cottontail 1.3649 0.1031 Inf
## California Ground Squirrel - Desert Iguana nonEst NA NA
## California Ground Squirrel - Giant Kangaroo Rat 1.3649 0.1031 Inf
## California Ground Squirrel - Great White Egret nonEst NA NA
## California Ground Squirrel - Greater Roadrunner 3.4965 0.2713 Inf
## California Ground Squirrel - Heermann's Kangaroo Rat -2.2378 0.0489 Inf
## California Ground Squirrel - Killdeer nonEst NA NA
## California Ground Squirrel - Kit Fox 3.3630 0.2543 Inf
## California Ground Squirrel - Lark Sparrow 3.4965 0.2713 Inf
## California Ground Squirrel - Loggerhead Shrike 3.3630 0.2543 Inf
## California Ground Squirrel - Mohave Ground Squirrel nonEst NA NA
## California Ground Squirrel - Mourning Dove 3.8330 0.3196 Inf
## California Ground Squirrel - Nelson's Antelope Squirrel 0.4866 0.0754 Inf
## California Ground Squirrel - (Red-tailed Hawk) nonEst NA NA
## California Ground Squirrel - Salinas Pocket Mouse nonEst NA NA
## California Ground Squirrel - Vesper Sparrow nonEst NA NA
## California Pocket Mouse - California Quail -1.7918 0.3819 Inf
## California Pocket Mouse - California Thrasher nonEst NA NA
## California Pocket Mouse - Common Raven -2.6027 0.3664 Inf
## California Pocket Mouse - Coyote -3.1023 0.3614 Inf
## California Pocket Mouse - Desert Cottontail -2.6912 0.3653 Inf
## California Pocket Mouse - Desert Iguana nonEst NA NA
## California Pocket Mouse - Giant Kangaroo Rat -2.6912 0.3653 Inf
## California Pocket Mouse - Great White Egret nonEst NA NA
## California Pocket Mouse - Greater Roadrunner -0.5596 0.4432 Inf
## California Pocket Mouse - Heermann's Kangaroo Rat -6.2939 0.3539 Inf
## California Pocket Mouse - Killdeer nonEst NA NA
## California Pocket Mouse - Kit Fox -0.6931 0.4330 Inf
## California Pocket Mouse - Lark Sparrow -0.5596 0.4432 Inf
## California Pocket Mouse - Loggerhead Shrike -0.6931 0.4330 Inf
## California Pocket Mouse - Mohave Ground Squirrel nonEst NA NA
## California Pocket Mouse - Mourning Dove -0.2231 0.4743 Inf
## California Pocket Mouse - Nelson's Antelope Squirrel -3.5695 0.3585 Inf
## California Pocket Mouse - (Red-tailed Hawk) nonEst NA NA
## California Pocket Mouse - Salinas Pocket Mouse nonEst NA NA
## California Pocket Mouse - Vesper Sparrow nonEst NA NA
## California Quail - California Thrasher nonEst NA NA
## California Quail - Common Raven -0.8109 0.1735 Inf
## California Quail - Coyote -1.3106 0.1626 Inf
## California Quail - Desert Cottontail -0.8995 0.1712 Inf
## California Quail - Desert Iguana nonEst NA NA
## California Quail - Giant Kangaroo Rat -0.8995 0.1712 Inf
## California Quail - Great White Egret nonEst NA NA
## California Quail - Greater Roadrunner 1.2321 0.3037 Inf
## California Quail - Heermann's Kangaroo Rat -4.5021 0.1451 Inf
## California Quail - Killdeer nonEst NA NA
## California Quail - Kit Fox 1.0986 0.2887 Inf
## California Quail - Lark Sparrow 1.2321 0.3037 Inf
## California Quail - Loggerhead Shrike 1.0986 0.2887 Inf
## California Quail - Mohave Ground Squirrel nonEst NA NA
## California Quail - Mourning Dove 1.5686 0.3476 Inf
## California Quail - Nelson's Antelope Squirrel -1.7778 0.1561 Inf
## California Quail - (Red-tailed Hawk) nonEst NA NA
## California Quail - Salinas Pocket Mouse nonEst NA NA
## California Quail - Vesper Sparrow nonEst NA NA
## California Thrasher - Common Raven nonEst NA NA
## California Thrasher - Coyote nonEst NA NA
## California Thrasher - Desert Cottontail nonEst NA NA
## California Thrasher - Desert Iguana nonEst NA NA
## California Thrasher - Giant Kangaroo Rat nonEst NA NA
## California Thrasher - Great White Egret nonEst NA NA
## California Thrasher - Greater Roadrunner nonEst NA NA
## California Thrasher - Heermann's Kangaroo Rat nonEst NA NA
## California Thrasher - Killdeer nonEst NA NA
## California Thrasher - Kit Fox nonEst NA NA
## California Thrasher - Lark Sparrow nonEst NA NA
## California Thrasher - Loggerhead Shrike nonEst NA NA
## California Thrasher - Mohave Ground Squirrel nonEst NA NA
## California Thrasher - Mourning Dove nonEst NA NA
## California Thrasher - Nelson's Antelope Squirrel nonEst NA NA
## California Thrasher - (Red-tailed Hawk) nonEst NA NA
## California Thrasher - Salinas Pocket Mouse nonEst NA NA
## California Thrasher - Vesper Sparrow nonEst NA NA
## Common Raven - Coyote -0.4997 0.1220 Inf
## Common Raven - Desert Cottontail -0.0886 0.1332 Inf
## Common Raven - Desert Iguana nonEst NA NA
## Common Raven - Giant Kangaroo Rat -0.0886 0.1332 Inf
## Common Raven - Great White Egret nonEst NA NA
## Common Raven - Greater Roadrunner 2.0431 0.2841 Inf
## Common Raven - Heermann's Kangaroo Rat -3.6912 0.0974 Inf
## Common Raven - Killdeer nonEst NA NA
## Common Raven - Kit Fox 1.9095 0.2679 Inf
## Common Raven - Lark Sparrow 2.0431 0.2841 Inf
## Common Raven - Loggerhead Shrike 1.9095 0.2679 Inf
## Common Raven - Mohave Ground Squirrel nonEst NA NA
## Common Raven - Mourning Dove 2.3795 0.3305 Inf
## Common Raven - Nelson's Antelope Squirrel -0.9668 0.1131 Inf
## Common Raven - (Red-tailed Hawk) nonEst NA NA
## Common Raven - Salinas Pocket Mouse nonEst NA NA
## Common Raven - Vesper Sparrow nonEst NA NA
## Coyote - Desert Cottontail 0.4111 0.1187 Inf
## Coyote - Desert Iguana nonEst NA NA
## Coyote - Giant Kangaroo Rat 0.4111 0.1187 Inf
## Coyote - Great White Egret nonEst NA NA
## Coyote - Greater Roadrunner 2.5427 0.2776 Inf
## Coyote - Heermann's Kangaroo Rat -3.1915 0.0765 Inf
## Coyote - Killdeer nonEst NA NA
## Coyote - Kit Fox 2.4092 0.2610 Inf
## Coyote - Lark Sparrow 2.5427 0.2776 Inf
## Coyote - Loggerhead Shrike 2.4092 0.2610 Inf
## Coyote - Mohave Ground Squirrel nonEst NA NA
## Coyote - Mourning Dove 2.8792 0.3250 Inf
## Coyote - Nelson's Antelope Squirrel -0.4672 0.0956 Inf
## Coyote - (Red-tailed Hawk) nonEst NA NA
## Coyote - Salinas Pocket Mouse nonEst NA NA
## Coyote - Vesper Sparrow nonEst NA NA
## Desert Cottontail - Desert Iguana nonEst NA NA
## Desert Cottontail - Giant Kangaroo Rat 0.0000 0.1302 Inf
## Desert Cottontail - Great White Egret nonEst NA NA
## Desert Cottontail - Greater Roadrunner 2.1316 0.2827 Inf
## Desert Cottontail - Heermann's Kangaroo Rat -3.6026 0.0933 Inf
## Desert Cottontail - Killdeer nonEst NA NA
## Desert Cottontail - Kit Fox 1.9981 0.2664 Inf
## Desert Cottontail - Lark Sparrow 2.1316 0.2827 Inf
## Desert Cottontail - Loggerhead Shrike 1.9981 0.2664 Inf
## Desert Cottontail - Mohave Ground Squirrel nonEst NA NA
## Desert Cottontail - Mourning Dove 2.4681 0.3294 Inf
## Desert Cottontail - Nelson's Antelope Squirrel -0.8783 0.1095 Inf
## Desert Cottontail - (Red-tailed Hawk) nonEst NA NA
## Desert Cottontail - Salinas Pocket Mouse nonEst NA NA
## Desert Cottontail - Vesper Sparrow nonEst NA NA
## Desert Iguana - Giant Kangaroo Rat nonEst NA NA
## Desert Iguana - Great White Egret nonEst NA NA
## Desert Iguana - Greater Roadrunner nonEst NA NA
## Desert Iguana - Heermann's Kangaroo Rat nonEst NA NA
## Desert Iguana - Killdeer nonEst NA NA
## Desert Iguana - Kit Fox nonEst NA NA
## Desert Iguana - Lark Sparrow nonEst NA NA
## Desert Iguana - Loggerhead Shrike nonEst NA NA
## Desert Iguana - Mohave Ground Squirrel nonEst NA NA
## Desert Iguana - Mourning Dove nonEst NA NA
## Desert Iguana - Nelson's Antelope Squirrel nonEst NA NA
## Desert Iguana - (Red-tailed Hawk) nonEst NA NA
## Desert Iguana - Salinas Pocket Mouse nonEst NA NA
## Desert Iguana - Vesper Sparrow nonEst NA NA
## Giant Kangaroo Rat - Great White Egret nonEst NA NA
## Giant Kangaroo Rat - Greater Roadrunner 2.1316 0.2827 Inf
## Giant Kangaroo Rat - Heermann's Kangaroo Rat -3.6026 0.0933 Inf
## Giant Kangaroo Rat - Killdeer nonEst NA NA
## Giant Kangaroo Rat - Kit Fox 1.9981 0.2664 Inf
## Giant Kangaroo Rat - Lark Sparrow 2.1316 0.2827 Inf
## Giant Kangaroo Rat - Loggerhead Shrike 1.9981 0.2664 Inf
## Giant Kangaroo Rat - Mohave Ground Squirrel nonEst NA NA
## Giant Kangaroo Rat - Mourning Dove 2.4681 0.3294 Inf
## Giant Kangaroo Rat - Nelson's Antelope Squirrel -0.8783 0.1095 Inf
## Giant Kangaroo Rat - (Red-tailed Hawk) nonEst NA NA
## Giant Kangaroo Rat - Salinas Pocket Mouse nonEst NA NA
## Giant Kangaroo Rat - Vesper Sparrow nonEst NA NA
## Great White Egret - Greater Roadrunner nonEst NA NA
## Great White Egret - Heermann's Kangaroo Rat nonEst NA NA
## Great White Egret - Killdeer nonEst NA NA
## Great White Egret - Kit Fox nonEst NA NA
## Great White Egret - Lark Sparrow nonEst NA NA
## Great White Egret - Loggerhead Shrike nonEst NA NA
## Great White Egret - Mohave Ground Squirrel nonEst NA NA
## Great White Egret - Mourning Dove nonEst NA NA
## Great White Egret - Nelson's Antelope Squirrel nonEst NA NA
## Great White Egret - (Red-tailed Hawk) nonEst NA NA
## Great White Egret - Salinas Pocket Mouse nonEst NA NA
## Great White Egret - Vesper Sparrow nonEst NA NA
## Greater Roadrunner - Heermann's Kangaroo Rat -5.7343 0.2677 Inf
## Greater Roadrunner - Killdeer nonEst NA NA
## Greater Roadrunner - Kit Fox -0.1335 0.3660 Inf
## Greater Roadrunner - Lark Sparrow 0.0000 0.3780 Inf
## Greater Roadrunner - Loggerhead Shrike -0.1335 0.3660 Inf
## Greater Roadrunner - Mohave Ground Squirrel nonEst NA NA
## Greater Roadrunner - Mourning Dove 0.3365 0.4140 Inf
## Greater Roadrunner - Nelson's Antelope Squirrel -3.0099 0.2738 Inf
## Greater Roadrunner - (Red-tailed Hawk) nonEst NA NA
## Greater Roadrunner - Salinas Pocket Mouse nonEst NA NA
## Greater Roadrunner - Vesper Sparrow nonEst NA NA
## Heermann's Kangaroo Rat - Killdeer nonEst NA NA
## Heermann's Kangaroo Rat - Kit Fox 5.6007 0.2505 Inf
## Heermann's Kangaroo Rat - Lark Sparrow 5.7343 0.2677 Inf
## Heermann's Kangaroo Rat - Loggerhead Shrike 5.6007 0.2505 Inf
## Heermann's Kangaroo Rat - Mohave Ground Squirrel nonEst NA NA
## Heermann's Kangaroo Rat - Mourning Dove 6.0707 0.3166 Inf
## Heermann's Kangaroo Rat - Nelson's Antelope Squirrel 2.7243 0.0613 Inf
## Heermann's Kangaroo Rat - (Red-tailed Hawk) nonEst NA NA
## Heermann's Kangaroo Rat - Salinas Pocket Mouse nonEst NA NA
## Heermann's Kangaroo Rat - Vesper Sparrow nonEst NA NA
## Killdeer - Kit Fox nonEst NA NA
## Killdeer - Lark Sparrow nonEst NA NA
## Killdeer - Loggerhead Shrike nonEst NA NA
## Killdeer - Mohave Ground Squirrel nonEst NA NA
## Killdeer - Mourning Dove nonEst NA NA
## Killdeer - Nelson's Antelope Squirrel nonEst NA NA
## Killdeer - (Red-tailed Hawk) nonEst NA NA
## Killdeer - Salinas Pocket Mouse nonEst NA NA
## Killdeer - Vesper Sparrow nonEst NA NA
## Kit Fox - Lark Sparrow 0.1335 0.3660 Inf
## Kit Fox - Loggerhead Shrike 0.0000 0.3536 Inf
## Kit Fox - Mohave Ground Squirrel nonEst NA NA
## Kit Fox - Mourning Dove 0.4700 0.4031 Inf
## Kit Fox - Nelson's Antelope Squirrel -2.8764 0.2569 Inf
## Kit Fox - (Red-tailed Hawk) nonEst NA NA
## Kit Fox - Salinas Pocket Mouse nonEst NA NA
## Kit Fox - Vesper Sparrow nonEst NA NA
## Lark Sparrow - Loggerhead Shrike -0.1335 0.3660 Inf
## Lark Sparrow - Mohave Ground Squirrel nonEst NA NA
## Lark Sparrow - Mourning Dove 0.3365 0.4140 Inf
## Lark Sparrow - Nelson's Antelope Squirrel -3.0099 0.2738 Inf
## Lark Sparrow - (Red-tailed Hawk) nonEst NA NA
## Lark Sparrow - Salinas Pocket Mouse nonEst NA NA
## Lark Sparrow - Vesper Sparrow nonEst NA NA
## Loggerhead Shrike - Mohave Ground Squirrel nonEst NA NA
## Loggerhead Shrike - Mourning Dove 0.4700 0.4031 Inf
## Loggerhead Shrike - Nelson's Antelope Squirrel -2.8764 0.2569 Inf
## Loggerhead Shrike - (Red-tailed Hawk) nonEst NA NA
## Loggerhead Shrike - Salinas Pocket Mouse nonEst NA NA
## Loggerhead Shrike - Vesper Sparrow nonEst NA NA
## Mohave Ground Squirrel - Mourning Dove nonEst NA NA
## Mohave Ground Squirrel - Nelson's Antelope Squirrel nonEst NA NA
## Mohave Ground Squirrel - (Red-tailed Hawk) nonEst NA NA
## Mohave Ground Squirrel - Salinas Pocket Mouse nonEst NA NA
## Mohave Ground Squirrel - Vesper Sparrow nonEst NA NA
## Mourning Dove - Nelson's Antelope Squirrel -3.3464 0.3217 Inf
## Mourning Dove - (Red-tailed Hawk) nonEst NA NA
## Mourning Dove - Salinas Pocket Mouse nonEst NA NA
## Mourning Dove - Vesper Sparrow nonEst NA NA
## Nelson's Antelope Squirrel - (Red-tailed Hawk) nonEst NA NA
## Nelson's Antelope Squirrel - Salinas Pocket Mouse nonEst NA NA
## Nelson's Antelope Squirrel - Vesper Sparrow nonEst NA NA
## (Red-tailed Hawk) - Salinas Pocket Mouse nonEst NA NA
## (Red-tailed Hawk) - Vesper Sparrow nonEst NA NA
## Salinas Pocket Mouse - Vesper Sparrow nonEst NA NA
## z.ratio p.value
## NA NA
## NA NA
## NA NA
## NA NA
## NA NA
## NA NA
## NA NA
## NA NA
## NA NA
## NA NA
## NA NA
## NA NA
## NA NA
## NA NA
## NA NA
## NA NA
## NA NA
## NA NA
## NA NA
## NA NA
## NA NA
## NA NA
## NA NA
## NA NA
## NA NA
## NA NA
## 10.572 <.0001
## 12.485 <.0001
## 14.127 <.0001
## 0.000 1.0000
## 11.374 <.0001
## 14.931 <.0001
## NA NA
## 13.598 <.0001
## 10.812 <.0001
## 13.233 <.0001
## NA NA
## 13.233 <.0001
## NA NA
## 12.889 <.0001
## -45.721 <.0001
## NA NA
## 13.225 <.0001
## 12.889 <.0001
## 13.225 <.0001
## NA NA
## 11.992 <.0001
## 6.453 <.0001
## NA NA
## NA NA
## NA NA
## -1.386 0.9999
## -3.037 0.3141
## -10.572 <.0001
## -0.533 1.0000
## -4.802 0.0005
## NA NA
## -6.891 <.0001
## -8.167 <.0001
## -7.118 <.0001
## NA NA
## -7.118 <.0001
## NA NA
## -1.736 0.9948
## -16.110 <.0001
## NA NA
## -2.049 0.9559
## -1.736 0.9948
## -2.049 0.9559
## NA NA
## -0.989 1.0000
## -9.350 <.0001
## NA NA
## NA NA
## NA NA
## -1.961 0.9737
## -12.485 <.0001
## 0.888 1.0000
## -4.295 0.0051
## NA NA
## -7.221 <.0001
## -9.042 <.0001
## -7.544 <.0001
## NA NA
## -7.544 <.0001
## NA NA
## -0.392 1.0000
## -20.370 <.0001
## NA NA
## -0.753 1.0000
## -0.392 1.0000
## -0.753 1.0000
## NA NA
## 0.426 1.0000
## -10.736 <.0001
## NA NA
## NA NA
## NA NA
## -14.127 <.0001
## 2.691 0.5827
## -2.773 0.5157
## NA NA
## -6.665 <.0001
## -9.215 <.0001
## -7.112 <.0001
## NA NA
## -7.112 <.0001
## NA NA
## 1.603 0.9985
## -25.381 <.0001
## NA NA
## 1.256 1.0000
## 1.603 0.9985
## 1.256 1.0000
## NA NA
## 2.326 0.8464
## -11.624 <.0001
## NA NA
## NA NA
## NA NA
## 11.374 <.0001
## 14.931 <.0001
## NA NA
## 13.598 <.0001
## 10.812 <.0001
## 13.233 <.0001
## NA NA
## 13.233 <.0001
## NA NA
## 12.889 <.0001
## -45.721 <.0001
## NA NA
## 13.225 <.0001
## 12.889 <.0001
## 13.225 <.0001
## NA NA
## 11.992 <.0001
## 6.453 <.0001
## NA NA
## NA NA
## NA NA
## -4.692 0.0009
## NA NA
## -7.103 <.0001
## -8.584 <.0001
## -7.366 <.0001
## NA NA
## -7.366 <.0001
## NA NA
## -1.263 1.0000
## -17.785 <.0001
## NA NA
## -1.601 0.9985
## -1.263 1.0000
## -1.601 0.9985
## NA NA
## -0.470 1.0000
## -9.957 <.0001
## NA NA
## NA NA
## NA NA
## NA NA
## -4.675 0.0009
## -8.058 <.0001
## -5.254 0.0001
## NA NA
## -5.254 0.0001
## NA NA
## 4.056 0.0135
## -31.020 <.0001
## NA NA
## 3.806 0.0343
## 4.056 0.0135
## 3.806 0.0343
## NA NA
## 4.513 0.0020
## -11.392 <.0001
## NA NA
## NA NA
## NA NA
## NA NA
## NA NA
## NA NA
## NA NA
## NA NA
## NA NA
## NA NA
## NA NA
## NA NA
## NA NA
## NA NA
## NA NA
## NA NA
## NA NA
## NA NA
## NA NA
## NA NA
## NA NA
## -4.096 0.0115
## -0.665 1.0000
## NA NA
## -0.665 1.0000
## NA NA
## 7.192 <.0001
## -37.890 <.0001
## NA NA
## 7.128 <.0001
## 7.192 <.0001
## 7.128 <.0001
## NA NA
## 7.199 <.0001
## -8.552 <.0001
## NA NA
## NA NA
## NA NA
## 3.463 0.1049
## NA NA
## 3.463 0.1049
## NA NA
## 9.161 <.0001
## -41.731 <.0001
## NA NA
## 9.231 <.0001
## 9.161 <.0001
## 9.231 <.0001
## NA NA
## 8.859 <.0001
## -4.887 0.0003
## NA NA
## NA NA
## NA NA
## NA NA
## 0.000 1.0000
## NA NA
## 7.541 <.0001
## -38.612 <.0001
## NA NA
## 7.500 <.0001
## 7.541 <.0001
## 7.500 <.0001
## NA NA
## 7.494 <.0001
## -8.019 <.0001
## NA NA
## NA NA
## NA NA
## NA NA
## NA NA
## NA NA
## NA NA
## NA NA
## NA NA
## NA NA
## NA NA
## NA NA
## NA NA
## NA NA
## NA NA
## NA NA
## NA NA
## NA NA
## 7.541 <.0001
## -38.612 <.0001
## NA NA
## 7.500 <.0001
## 7.541 <.0001
## 7.500 <.0001
## NA NA
## 7.494 <.0001
## -8.019 <.0001
## NA NA
## NA NA
## NA NA
## NA NA
## NA NA
## NA NA
## NA NA
## NA NA
## NA NA
## NA NA
## NA NA
## NA NA
## NA NA
## NA NA
## NA NA
## -21.421 <.0001
## NA NA
## -0.365 1.0000
## 0.000 1.0000
## -0.365 1.0000
## NA NA
## 0.813 1.0000
## -10.994 <.0001
## NA NA
## NA NA
## NA NA
## NA NA
## 22.362 <.0001
## 21.421 <.0001
## 22.362 <.0001
## NA NA
## 19.175 <.0001
## 44.476 <.0001
## NA NA
## NA NA
## NA NA
## NA NA
## NA NA
## NA NA
## NA NA
## NA NA
## NA NA
## NA NA
## NA NA
## NA NA
## 0.365 1.0000
## 0.000 1.0000
## NA NA
## 1.166 1.0000
## -11.195 <.0001
## NA NA
## NA NA
## NA NA
## -0.365 1.0000
## NA NA
## 0.813 1.0000
## -10.994 <.0001
## NA NA
## NA NA
## NA NA
## NA NA
## 1.166 1.0000
## -11.195 <.0001
## NA NA
## NA NA
## NA NA
## NA NA
## NA NA
## NA NA
## NA NA
## NA NA
## -10.401 <.0001
## NA NA
## NA NA
## NA NA
## NA NA
## NA NA
## NA NA
## NA NA
## NA NA
## NA NA
##
## Results are averaged over the levels of: microsite
## Results are given on the log (not the response) scale.
## P value adjustment: tukey method for comparing a family of 27 estimates
animals_density <- photo %>% group_by(site_code,microsite,plot, shrub_density, common_name) %>% summarise(captures = sum(animal.hit))
## `summarise()` has grouped output by 'site_code', 'microsite', 'plot',
## 'shrub_density'. You can override using the `.groups` argument.
animals_density <- animals_density %>% filter(common_name != "Blank") %>% filter(common_name != "No CV Result")
### PCA
library(vegan)
## Loading required package: permute
## Loading required package: lattice
## This is vegan 2.6-4
library(ape) ### For PCOA
##
## Attaching package: 'ape'
## The following object is masked from 'package:ggpubr':
##
## rotate
## The following object is masked from 'package:dplyr':
##
## where
pca_data <- animals_density ### Created new df for pca data
pca_data <- pca_data %>%
spread(common_name, captures) %>%
ungroup() %>%
dplyr::select(-site_code, -microsite, -plot) %>%
replace(is.na(.),0)
dim(pca_data)
## [1] 22 28
env <- read.csv("environment.csv") ### Drop Tecopa open 1, Tecopa open 4, since they have no animal observations.
dim(env)
## [1] 22 5
m01 <- adonis(pca_data ~ microsite*shrub_density, data = env)
## 'adonis' will be deprecated: use 'adonis2' instead
m01
## $aov.tab
## Permutation: free
## Number of permutations: 999
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## microsite 1 0.2242 0.22425 0.76358 0.03600 0.613
## shrub_density 1 0.4245 0.42452 1.44550 0.06815 0.211
## Residuals 19 5.5799 0.29368 0.89584
## Total 21 6.2287 1.00000
##
## $call
## adonis(formula = pca_data ~ microsite * shrub_density, data = env)
##
## $coefficients
## shrub_density American Robin Black-tailed Jackrabbit
## (Intercept) -4.757692 5.000000e-02 -13.455128
## microsite1 -6.857692 -5.000000e-02 -18.955128
## shrub_density 1.876923 1.823528e-17 4.184615
## microsite1:shrub_density NA NA NA
## Blunt-nosed Leopard Lizard Bobcat
## (Intercept) 0.9987179 -0.6602564
## microsite1 0.8987179 -1.1602564
## shrub_density -0.1538462 0.1692308
## microsite1:shrub_density NA NA
## Brewer's Blackbird California Ground Squirrel
## (Intercept) 0.18717949 20.411538
## microsite1 -0.21282051 13.511538
## shrub_density 0.06153846 -1.815385
## microsite1:shrub_density NA NA
## California Pocket Mouse California Quail
## (Intercept) 0.35641026 -4.2487179
## microsite1 0.15641026 -5.6487179
## shrub_density -0.03076923 0.9538462
## microsite1:shrub_density NA NA
## California Thrasher Common Raven Coyote
## (Intercept) -1.4743590 0.7910256 1.4923077
## microsite1 -1.4743590 -3.5089744 -4.1076923
## shrub_density 0.2769231 0.3230769 0.4769231
## microsite1:shrub_density NA NA NA
## Desert Cottontail Desert Iguana Giant Kangaroo Rat
## (Intercept) -24.729487 2.07820513 6.9935897
## microsite1 -26.629487 -1.82179487 4.4935897
## shrub_density 4.861538 -0.01538462 -0.7692308
## microsite1:shrub_density NA NA NA
## Great White Egret Greater Roadrunner
## (Intercept) 5.000000e-02 -4.3730769
## microsite1 -5.000000e-02 -4.4730769
## shrub_density 1.076807e-17 0.8307692
## microsite1:shrub_density NA NA
## Heermann's Kangaroo Rat Killdeer Kit Fox
## (Intercept) 140.330769 5.000000e-02 0.44487179
## microsite1 59.130769 -5.000000e-02 0.14487179
## shrub_density -7.707692 6.937479e-18 -0.01538462
## microsite1:shrub_density NA NA NA
## Lark Sparrow Loggerhead Shrike Mohave Ground Squirrel
## (Intercept) -1.8384615 -0.5935897 0.81923077
## microsite1 -2.2384615 -0.8935897 0.71923077
## shrub_density 0.3846154 0.1692308 -0.09230769
## microsite1:shrub_density NA NA NA
## Mourning Dove Nelson's Antelope Squirrel
## (Intercept) -1.9384615 18.984615
## microsite1 -2.1384615 13.784615
## shrub_density 0.3846154 -2.246154
## microsite1:shrub_density NA NA
## Red-tailed Hawk Salinas Pocket Mouse Vesper Sparrow
## (Intercept) -0.5641026 5.000000e-02 5.000000e-02
## microsite1 -0.5641026 -5.000000e-02 -5.000000e-02
## shrub_density 0.1076923 1.076807e-17 6.937479e-18
## microsite1:shrub_density NA NA NA
##
## $coef.sites
## 1 2 3 4
## (Intercept) 0.72187241 0.84634032 1.11978420 0.78585052
## microsite1 0.19194029 0.33115230 0.42744428 0.26477687
## shrub_density -0.03063205 -0.05640075 -0.06512504 -0.03711672
## microsite1:shrub_density NA NA NA NA
## 5 6 7 8
## (Intercept) 0.7805666 0.70692759 0.95262164 0.80543797
## microsite1 0.2293742 0.15739580 0.22071669 0.25186788
## shrub_density -0.0393115 -0.02414532 -0.02857079 -0.03727318
## microsite1:shrub_density NA NA NA NA
## 9 10 11 12
## (Intercept) 1.7428122 1.4970441 0.99852391 1.06366871
## microsite1 1.0220992 0.8596173 0.44864549 0.53735308
## shrub_density -0.1847971 -0.1548735 -0.07052619 -0.08656954
## microsite1:shrub_density NA NA NA NA
## 13 14 15 16
## (Intercept) 0.82385406 0.795551518 0.79238103 0.84065136
## microsite1 0.12690322 0.021850057 0.21124835 0.30308675
## shrub_density -0.02568071 -0.008853955 -0.02915258 -0.04325977
## microsite1:shrub_density NA NA NA NA
## 17 18 19 20
## (Intercept) 0.38555800 0.50980072 0.68786923 0.55515661
## microsite1 -0.59216512 -0.43255379 -0.20343288 -0.30788751
## shrub_density 0.09323238 0.06736799 0.02712249 0.04455634
## microsite1:shrub_density NA NA NA NA
## 21 22
## (Intercept) 0.881696341 0.64736295
## microsite1 0.013954406 -0.20302121
## shrub_density -0.001421805 0.02954886
## microsite1:shrub_density NA NA
##
## $f.perms
## [,1] [,2]
## [1,] 1.17523680 1.35296523
## [2,] 0.98166642 0.78004529
## [3,] 0.40043152 1.55001892
## [4,] 0.31585544 0.82015476
## [5,] 1.77201134 3.11595392
## [6,] 1.23814875 2.15375009
## [7,] 0.37267057 0.81109910
## [8,] 1.04940680 0.75474965
## [9,] 1.50863219 1.93679319
## [10,] 0.65908961 1.64324425
## [11,] 0.62490843 0.61053335
## [12,] 1.72810053 2.81913133
## [13,] 0.56427845 1.07807206
## [14,] 0.32257652 0.42267195
## [15,] 0.42170264 0.72860630
## [16,] 1.08603625 1.13489663
## [17,] 1.75691225 0.63314896
## [18,] 0.37031606 1.31843237
## [19,] 0.81095298 2.16315222
## [20,] 2.53012166 1.45869479
## [21,] 1.16418008 0.88624093
## [22,] 0.44793398 0.58256970
## [23,] 1.55247004 0.19280301
## [24,] 0.82778860 1.93641046
## [25,] 1.48826244 3.68241342
## [26,] 0.56990525 1.72230221
## [27,] 0.22126505 1.22268318
## [28,] 0.34594658 1.03154611
## [29,] 2.63897564 1.96627396
## [30,] 0.76933751 0.39674199
## [31,] 0.57609944 1.73548537
## [32,] 1.32117359 1.44365393
## [33,] 0.94641346 0.95836573
## [34,] 0.92627584 3.33801241
## [35,] 1.40526510 1.36674159
## [36,] 2.17450145 0.86843106
## [37,] 0.50154215 1.08691669
## [38,] 1.11395102 0.71755676
## [39,] 0.64133810 0.87063706
## [40,] 0.43348240 0.36842641
## [41,] 1.43259538 1.14958849
## [42,] 1.25117092 0.74648519
## [43,] 0.77958352 0.36754982
## [44,] 0.92504011 1.60079349
## [45,] 0.55793059 0.84906242
## [46,] 1.12795379 1.40404071
## [47,] 0.75102598 0.23198349
## [48,] 0.24981999 1.41797157
## [49,] 1.03430620 1.57316694
## [50,] 1.31815263 0.96738647
## [51,] 0.98454396 0.26428966
## [52,] 0.32770954 0.75141108
## [53,] 0.88856952 1.51162614
## [54,] 0.98978911 0.32633876
## [55,] 1.72449280 1.03682873
## [56,] 0.78350859 0.96128812
## [57,] 0.99896637 0.79036131
## [58,] 1.95992400 1.49567258
## [59,] 1.17256443 0.56493397
## [60,] 1.73408435 1.38771636
## [61,] 2.40040427 0.94766646
## [62,] 0.65001742 1.82871817
## [63,] 0.88626891 1.63947519
## [64,] 1.85930496 1.19317141
## [65,] 0.74873396 1.12989295
## [66,] 0.86150850 1.24418373
## [67,] 0.54840433 2.54703291
## [68,] 0.40323732 0.92932382
## [69,] 1.32357596 1.67204490
## [70,] 0.55850815 0.41373430
## [71,] 0.67077213 1.49705641
## [72,] 0.71988173 0.96679118
## [73,] 1.69939153 1.23519182
## [74,] 0.59986991 0.70684895
## [75,] 0.74801044 0.87525509
## [76,] 0.54157497 0.55964961
## [77,] 1.10588050 1.01637393
## [78,] 0.36061275 0.75608988
## [79,] 0.88592193 1.01024122
## [80,] 0.35030525 0.82622292
## [81,] 0.46448632 1.26086788
## [82,] 0.76056464 0.48575366
## [83,] 1.21124575 0.85114903
## [84,] 2.21803356 1.50961213
## [85,] 0.39878234 0.72572007
## [86,] 1.31642125 0.86826125
## [87,] 1.83692043 0.33226833
## [88,] 0.83305641 0.72073502
## [89,] 1.97551244 2.78762546
## [90,] 0.29906903 0.52789401
## [91,] 0.52455266 0.18369849
## [92,] 0.52828746 0.29119005
## [93,] 0.73460754 0.16090542
## [94,] 0.79779516 0.60638179
## [95,] 1.49942315 0.93383091
## [96,] 0.98669740 1.07460793
## [97,] 1.78769602 1.83056729
## [98,] 0.94901241 2.59566234
## [99,] 1.10579718 1.50049581
## [100,] 0.34039449 1.55964177
## [101,] 0.39380959 1.70449358
## [102,] 0.85243527 0.58430886
## [103,] 2.27098535 1.26212251
## [104,] 1.11178706 0.83923447
## [105,] 1.58612262 1.68470248
## [106,] 0.53677423 0.68768146
## [107,] 0.73353177 1.67882140
## [108,] 2.22572882 0.60095118
## [109,] 0.99709257 0.94447619
## [110,] 0.53319492 0.83939966
## [111,] 2.41463602 0.40208802
## [112,] 0.58081286 0.81602722
## [113,] 0.63422009 0.60187715
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## [566,] 0.72518270 0.70083522
## [567,] 1.97730714 1.48353610
## [568,] 0.61339377 0.60230637
## [569,] 1.26272008 0.93715118
## [570,] 1.30927414 0.46964080
## [571,] 1.12699290 0.94772178
## [572,] 1.24344421 0.55605700
## [573,] 1.32496264 0.58104911
## [574,] 0.56507243 0.65296869
## [575,] 0.64703120 1.88770231
## [576,] 1.85990798 1.43062866
## [577,] 0.29135822 0.50589377
## [578,] 0.64986406 1.01164589
## [579,] 0.35484062 0.45316557
## [580,] 1.35023328 1.10382884
## [581,] 2.53154606 0.59371789
## [582,] 1.13872609 0.47022346
## [583,] 0.75917874 1.33239704
## [584,] 0.49838294 0.15700202
## [585,] 0.50866843 0.26193047
## [586,] 3.15409376 1.72169043
## [587,] 0.33734340 1.46991976
## [588,] 0.56501898 0.77091406
## [589,] 0.62293061 0.94963357
## [590,] 1.67331708 0.22716815
## [591,] 1.22901258 1.85624448
## [592,] 0.66320444 1.46761900
## [593,] 0.83659890 0.76134750
## [594,] 0.64221536 1.32681412
## [595,] 0.82885062 1.51809024
## [596,] 0.79190090 0.25342829
## [597,] 0.59775526 1.09395343
## [598,] 0.80836624 1.54318415
## [599,] 0.97715318 0.65017385
## [600,] 1.75701552 1.21016212
## [601,] 0.36316350 0.95085420
## [602,] 1.55683907 2.09960702
## [603,] 2.57388304 1.10716120
## [604,] 1.55458935 0.92726058
## [605,] 0.95762323 0.48343748
## [606,] 1.76582874 0.97266205
## [607,] 1.38883288 0.24989493
## [608,] 0.65538037 0.45296996
## [609,] 0.93617188 0.14064169
## [610,] 2.12102062 0.57995649
## [611,] 0.85948520 0.74929649
## [612,] 0.99999627 0.52258887
## [613,] 1.23820543 1.31490570
## [614,] 0.63924282 1.03050087
## [615,] 0.67133002 2.30115908
## [616,] 0.48265403 0.97662108
## [617,] 0.68073342 0.21136436
## [618,] 0.73977290 0.55999427
## [619,] 0.76800931 0.46928623
## [620,] 0.39535526 1.49939201
## [621,] 1.48679568 0.76102426
## [622,] 0.61721593 1.20272311
## [623,] 1.19699189 1.84908388
## [624,] 0.50274933 1.01049000
## [625,] 0.43793861 0.83525693
## [626,] 1.17200106 2.01748224
## [627,] 1.44000993 3.21440390
## [628,] 0.62039356 1.14388505
## [629,] 1.74483156 1.05502994
## [630,] 2.49570535 1.24569365
## [631,] 1.03912247 0.53174027
## [632,] 0.98495924 1.41736741
## [633,] 1.08827550 0.81431498
## [634,] 0.53490192 0.73678205
## [635,] 0.91127201 0.91874067
## [636,] 1.42407021 0.72220187
## [637,] 1.49694118 1.10970357
## [638,] 0.58757881 0.44649731
## [639,] 2.86295019 0.85863404
## [640,] 0.35851694 0.15974849
## [641,] 1.60288532 0.80306939
## [642,] 1.56176456 2.11813896
## [643,] 0.47495502 0.94881176
## [644,] 0.72028651 0.82429600
## [645,] 0.79139013 1.10597134
## [646,] 0.99965111 1.30483872
## [647,] 0.99625837 1.52340682
## [648,] 0.76310790 0.25793294
## [649,] 0.51070863 2.13162717
## [650,] 1.24360316 0.42017577
## [651,] 0.96870084 1.22298601
## [652,] 2.27885280 1.90898342
## [653,] 1.05019326 2.19135882
## [654,] 1.16427785 1.32716642
## [655,] 0.58942901 0.94165831
## [656,] 1.10582776 1.83418872
## [657,] 0.73623854 0.60238667
## [658,] 0.61709768 1.30065636
## [659,] 0.91720332 0.83020850
## [660,] 0.68311778 1.76149403
## [661,] 0.62515130 0.24972418
## [662,] 1.12948872 1.54780284
## [663,] 0.72948025 2.39477677
## [664,] 1.00363150 1.10519312
## [665,] 0.67785095 0.53700381
## [666,] 1.28071466 1.41545439
## [667,] 1.05884759 0.38852455
## [668,] 1.11559977 0.64507027
## [669,] 1.54020245 0.85886993
## [670,] 1.82098332 2.42888064
## [671,] 0.56625069 0.30361552
## [672,] 0.77208519 0.90583939
## [673,] 1.21402475 1.28865204
## [674,] 0.39641304 0.77621175
## [675,] 3.20023921 0.71468148
## [676,] 0.33110363 0.36552667
## [677,] 0.78569329 0.55287691
## [678,] 1.03472093 1.68791736
## [679,] 0.59981066 0.45145275
## [680,] 1.25372313 0.42804081
## [681,] 0.56130797 0.72349538
## [682,] 0.97805016 0.20056039
## [683,] 1.25755978 1.13303714
## [684,] 1.55662155 1.26429428
## [685,] 0.96482092 1.29290288
## [686,] 1.97217397 1.23239968
## [687,] 0.94561033 0.44491337
## [688,] 1.46275076 1.45408908
## [689,] 1.03040272 0.92012240
## [690,] 0.66894744 0.33972767
## [691,] 0.38996065 1.00943533
## [692,] 1.70445191 2.18953498
## [693,] 0.25890657 1.27993679
## [694,] 1.57510328 0.70771356
## [695,] 0.38976537 1.40515497
## [696,] 0.48630517 0.90819073
## [697,] 0.83565184 1.17059170
## [698,] 0.81911199 0.68688753
## [699,] 1.13914773 0.72122473
## [700,] 0.91774625 0.85794496
## [701,] 1.32802193 0.75795965
## [702,] 0.20423270 0.77550499
## [703,] 0.93960530 0.98696003
## [704,] 0.36276554 0.86878709
## [705,] 0.70620026 0.79261311
## [706,] 0.57574611 1.86468542
## [707,] 1.66142610 0.70539651
## [708,] 0.74860791 0.09798201
## [709,] 0.80474051 1.29801926
## [710,] 1.53280980 0.50637859
## [711,] 1.18312332 1.17360605
## [712,] 0.43002511 0.89132148
## [713,] 2.12352662 3.05631797
## [714,] 0.57980470 0.37759132
## [715,] 1.75552511 0.78894223
## [716,] 0.93259410 3.69322613
## [717,] 0.36282548 1.09037971
## [718,] 1.07618058 1.12717943
## [719,] 0.09873381 1.47479316
## [720,] 0.80639479 1.57846277
## [721,] 0.64202314 0.77588871
## [722,] 1.70593230 0.27166893
## [723,] 0.41079682 1.37298102
## [724,] 1.50887501 0.66015466
## [725,] 0.48972043 0.82139081
## [726,] 0.64363274 0.46518961
## [727,] 1.23352959 0.39086926
## [728,] 0.55701108 1.25317866
## [729,] 0.99842203 0.47665427
## [730,] 1.74016927 1.59237041
## [731,] 1.41894862 2.27528721
## [732,] 1.11569411 0.44252006
## [733,] 0.81052451 0.95353860
## [734,] 0.20214119 0.86748258
## [735,] 1.72941893 0.24300497
## [736,] 0.52574987 0.51454899
## [737,] 1.36674169 1.54263097
## [738,] 1.74963943 0.81953420
## [739,] 0.55730680 0.56058573
## [740,] 1.19032826 0.65053090
## [741,] 0.74447654 1.29622045
## [742,] 0.77593508 2.22806557
## [743,] 0.83900034 0.95949426
## [744,] 0.48967800 0.85122919
## [745,] 0.55341840 1.11601410
## [746,] 0.37952663 0.57181675
## [747,] 0.35991730 1.04841444
## [748,] 0.72398703 1.37951468
## [749,] 2.12874397 2.59414519
## [750,] 1.01949123 0.35903435
## [751,] 1.44901680 2.10592601
## [752,] 1.03045023 1.73698673
## [753,] 0.51249534 0.62207465
## [754,] 1.43239337 0.40037155
## [755,] 0.36635589 0.49800044
## [756,] 0.74636218 1.19642822
## [757,] 0.55851671 0.96172439
## [758,] 2.12598738 0.46058575
## [759,] 0.60522949 1.37722129
## [760,] 0.30529500 0.99534790
## [761,] 0.63965936 0.64070421
## [762,] 0.65619947 0.61842989
## [763,] 1.01091590 0.78601271
## [764,] 1.40355326 1.19551393
## [765,] 1.21564866 0.36679531
## [766,] 0.85409101 0.29028972
## [767,] 1.87726642 1.26043115
## [768,] 1.16393399 0.66497737
## [769,] 0.21856651 1.08196892
## [770,] 1.60006921 2.14273829
## [771,] 1.28916058 0.04489146
## [772,] 1.09335096 0.77220704
## [773,] 2.49754985 1.16411738
## [774,] 0.84308850 0.48355307
## [775,] 0.57610741 0.78298841
## [776,] 0.91598832 2.16507791
## [777,] 1.35549141 1.18090788
## [778,] 1.68925770 0.35580667
## [779,] 1.30420393 0.61024550
## [780,] 0.81978560 0.85083482
## [781,] 0.74751483 1.09551801
## [782,] 0.44970679 1.65396479
## [783,] 0.39228568 0.82957215
## [784,] 0.96871531 0.75036342
## [785,] 0.93696692 0.71354203
## [786,] 1.16799486 1.45788091
## [787,] 1.81254892 0.28249386
## [788,] 0.78958134 1.07662551
## [789,] 0.63510515 0.83158946
## [790,] 1.62191388 0.17540344
## [791,] 0.18880649 1.10094347
## [792,] 0.90434415 2.87640494
## [793,] 2.02162548 2.07006381
## [794,] 0.73154752 2.69325674
## [795,] 0.66898363 3.31618945
## [796,] 1.06760855 1.16962276
## [797,] 1.19640608 0.49451362
## [798,] 2.03093366 0.56595644
## [799,] 0.46572679 1.02368717
## [800,] 1.05214980 0.82502482
## [801,] 0.75721304 1.11724561
## [802,] 0.61732702 0.50423426
## [803,] 0.49308337 0.64500441
## [804,] 1.25408350 1.01195523
## [805,] 0.47457884 0.38967851
## [806,] 1.67329616 1.37858203
## [807,] 0.41814306 1.32049028
## [808,] 1.19552488 1.45442785
## [809,] 0.21608401 1.30952967
## [810,] 0.90863959 1.27530502
## [811,] 0.42266523 0.83902064
## [812,] 0.89582300 1.18130488
## [813,] 1.06570840 0.32106213
## [814,] 0.47422793 0.79915133
## [815,] 0.82334560 1.72449000
## [816,] 2.07993735 2.80873061
## [817,] 1.36532165 1.03099851
## [818,] 0.74559791 0.30496085
## [819,] 1.63101140 1.03084772
## [820,] 1.82890042 0.49539438
## [821,] 0.50167640 1.06949814
## [822,] 1.10820422 0.39755712
## [823,] 3.60113438 1.10231620
## [824,] 0.61046498 0.59464982
## [825,] 0.89618263 0.77303798
## [826,] 0.19425026 0.88279368
## [827,] 1.45581650 0.36851172
## [828,] 1.34865693 1.20910735
## [829,] 0.67997913 1.03510920
## [830,] 0.15753690 1.13586975
## [831,] 0.51815806 5.07060245
## [832,] 0.86197224 1.57676229
## [833,] 0.87016897 1.00143647
## [834,] 2.60792495 0.71987487
## [835,] 0.74851582 2.19897186
## [836,] 0.58227823 1.74104118
## [837,] 2.61944370 0.25804724
## [838,] 1.10536478 0.58490870
## [839,] 1.44789672 0.56159512
## [840,] 1.85146414 0.33434120
## [841,] 4.32862918 0.79598682
## [842,] 0.51356999 0.80932130
## [843,] 0.53675353 2.26169681
## [844,] 0.77506538 0.68400654
## [845,] 2.09512740 2.57188969
## [846,] 1.12813599 0.65725054
## [847,] 0.98388449 0.99286705
## [848,] 1.92977156 0.13183909
## [849,] 1.81233036 0.73670361
## [850,] 1.24013624 0.57978796
## [851,] 1.26103379 2.59185615
## [852,] 0.88960938 0.79299764
## [853,] 1.19369683 1.80585742
## [854,] 0.90108683 0.41462820
## [855,] 1.37684057 0.43769633
## [856,] 0.98586972 1.30939148
## [857,] 1.56996122 1.22117732
## [858,] 4.05339997 0.18547662
## [859,] 0.87797854 1.24297202
## [860,] 0.74156459 0.99431027
## [861,] 1.28273759 0.27834571
## [862,] 1.11699922 1.01669600
## [863,] 0.49927639 1.51761768
## [864,] 0.87149275 0.77291511
## [865,] 0.79636929 1.05217305
## [866,] 0.54446958 1.88297141
## [867,] 1.35669203 1.69329747
## [868,] 0.79139083 1.48211506
## [869,] 0.79306310 1.61663396
## [870,] 0.53617231 0.72554824
## [871,] 1.12053032 1.08157228
## [872,] 0.52372152 0.53295293
## [873,] 1.57528613 0.97943284
## [874,] 0.53746371 1.32676842
## [875,] 1.34409397 0.82262489
## [876,] 0.58456068 0.80628174
## [877,] 2.03928822 1.15999369
## [878,] 0.61796567 0.24895819
## [879,] 0.71766067 1.30729581
## [880,] 1.11078495 1.58943801
## [881,] 1.11081999 0.90947617
## [882,] 1.62957992 1.08105628
## [883,] 0.56514122 0.61113211
## [884,] 0.69475297 1.16688313
## [885,] 0.93984112 1.60451026
## [886,] 0.81540812 1.24650606
## [887,] 1.25182059 0.61782899
## [888,] 0.23958564 0.69658715
## [889,] 1.47641671 1.10066063
## [890,] 0.64709202 1.45807253
## [891,] 1.51184358 1.15058309
## [892,] 2.49067817 1.79425208
## [893,] 1.40989005 0.37502444
## [894,] 0.80973226 1.31310551
## [895,] 1.44701571 1.09180800
## [896,] 1.94094805 1.29957325
## [897,] 1.85784339 0.70181365
## [898,] 0.83709056 0.70940252
## [899,] 0.31536371 2.28069925
## [900,] 0.25900435 1.93227606
## [901,] 0.40748051 0.91489648
## [902,] 0.65660895 1.12692690
## [903,] 0.82204061 1.03226988
## [904,] 1.32184354 1.24839304
## [905,] 0.79338500 0.67727147
## [906,] 0.66799530 1.32882336
## [907,] 0.97292020 1.31288996
## [908,] 1.48780221 1.81997846
## [909,] 0.62510881 0.77756576
## [910,] 0.46974454 0.48425767
## [911,] 1.96184752 0.76192068
## [912,] 0.73904997 2.04766600
## [913,] 1.13426238 1.14507762
## [914,] 0.70463026 1.05401694
## [915,] 0.67563564 1.51736256
## [916,] 1.31475657 2.20707052
## [917,] 0.17390875 1.36192766
## [918,] 1.48057759 0.92394053
## [919,] 0.42456894 0.85636976
## [920,] 2.27625767 1.26432540
## [921,] 0.87810189 0.67755399
## [922,] 1.06752545 1.06696469
## [923,] 1.80887284 1.01632172
## [924,] 1.16562092 0.85451967
## [925,] 1.92696983 0.37374084
## [926,] 1.34978578 0.96156596
## [927,] 0.48427818 2.19574435
## [928,] 1.14551138 2.08147284
## [929,] 0.75736130 1.11200852
## [930,] 1.19181157 0.11466078
## [931,] 1.32423546 1.11946874
## [932,] 0.78496369 0.87508383
## [933,] 1.30178089 1.05190178
## [934,] 0.98277570 0.57448775
## [935,] 1.25859957 0.97205832
## [936,] 1.86790922 0.68754858
## [937,] 1.29904495 1.01102824
## [938,] 1.26762078 0.87082992
## [939,] 0.35466133 0.20543129
## [940,] 0.49666318 3.45863482
## [941,] 0.92049659 1.85276146
## [942,] 1.18822034 1.64785113
## [943,] 1.40636548 2.45177260
## [944,] 0.77353591 1.25695198
## [945,] 0.95456539 0.53531649
## [946,] 1.35773434 3.00796492
## [947,] 0.75712650 0.60279750
## [948,] 1.16892249 1.21549117
## [949,] 0.69261752 1.77061875
## [950,] 0.62159658 1.03859306
## [951,] 0.33106356 0.70290055
## [952,] 0.39378655 0.31428341
## [953,] 1.03706635 0.97503533
## [954,] 0.77602159 0.51656912
## [955,] 0.72052906 0.82563454
## [956,] 0.63128675 1.81375383
## [957,] 0.68872843 0.73182739
## [958,] 0.75561275 0.23251083
## [959,] 1.20591225 0.59770677
## [960,] 1.58567718 1.15259835
## [961,] 0.53854870 0.64593568
## [962,] 0.41537872 0.64637812
## [963,] 0.78061421 1.51401713
## [964,] 0.18036516 2.13342315
## [965,] 0.82719421 1.11190985
## [966,] 0.24018955 0.99668062
## [967,] 0.41488357 1.30866447
## [968,] 1.64962595 0.97874453
## [969,] 1.46045392 0.44362278
## [970,] 0.66378657 0.68798715
## [971,] 2.68774740 0.25696372
## [972,] 1.68862370 0.70656443
## [973,] 0.88655645 0.56525996
## [974,] 1.04970407 0.49649119
## [975,] 0.27050448 0.93403399
## [976,] 0.91734601 0.49203899
## [977,] 1.90310819 1.05225374
## [978,] 0.18139904 0.96296166
## [979,] 1.10237024 0.50461132
## [980,] 0.86342220 1.47842263
## [981,] 1.03320975 0.51547367
## [982,] 0.50731128 1.06394333
## [983,] 1.18967425 1.02081122
## [984,] 1.83377541 0.99103334
## [985,] 2.61949013 1.45993651
## [986,] 1.01351881 1.03241760
## [987,] 1.11880029 1.04859155
## [988,] 0.36926907 1.44595535
## [989,] 0.23511625 1.45019684
## [990,] 1.49239145 0.50904018
## [991,] 0.79617557 2.58323579
## [992,] 0.92473477 0.50651023
## [993,] 0.80655065 1.11536448
## [994,] 0.85752977 1.04795680
## [995,] 1.37434670 0.75244726
## [996,] 0.80631858 0.36351018
## [997,] 0.66350688 0.41710199
## [998,] 0.45220216 1.93384597
## [999,] 1.42647962 1.48353223
##
## $model.matrix
## (Intercept) microsite1 shrub_density
## 1 1 1 11
## 2 1 1 12
## 3 1 -1 0
## 4 1 -1 0
## 5 1 1 11
## 6 1 1 10
## 7 1 -1 0
## 8 1 -1 0
## 9 1 1 14
## 10 1 1 13
## 11 1 -1 0
## 12 1 -1 0
## 13 1 1 11
## 14 1 1 11
## 15 1 -1 0
## 16 1 -1 0
## 17 1 1 10
## 18 1 1 11
## 19 1 1 11
## 20 1 1 10
## 21 1 -1 0
## 22 1 -1 0
##
## $terms
## pca_data ~ microsite * shrub_density
## attr(,"variables")
## list(pca_data, microsite, shrub_density)
## attr(,"factors")
## microsite shrub_density microsite:shrub_density
## pca_data 0 0 0
## microsite 1 0 1
## shrub_density 0 1 1
## attr(,"term.labels")
## [1] "microsite" "shrub_density"
## [3] "microsite:shrub_density"
## attr(,"order")
## [1] 1 1 2
## attr(,"intercept")
## [1] 1
## attr(,"response")
## [1] 1
## attr(,".Environment")
## <environment: R_GlobalEnv>
##
## attr(,"class")
## [1] "adonis"
dist <- vegdist(pca_data, species = "bray")
res <- pcoa(dist)
p1 <- as.data.frame(res$vectors)%>%
dplyr::select(Axis.1, Axis.2) %>%
bind_cols(env,.)
ggplot(p1, aes(Axis.1, Axis.2, group = microsite)) +
geom_point(aes(color = microsite)) +
geom_text(aes(label=plot), hjust = 0, vjust = 0, check_overlap = TRUE, nudge_x = 0.01)+
scale_color_brewer(palette = "Set1") +
labs(color = "", subtitle = "labels denote plot identity")
m02 <- betadisper(dist, env$microsite)
m02
##
## Homogeneity of multivariate dispersions
##
## Call: betadisper(d = dist, group = env$microsite)
##
## No. of Positive Eigenvalues: 15
## No. of Negative Eigenvalues: 6
##
## Average distance to median:
## Density Open
## 0.5063 0.4721
##
## Eigenvalues for PCoA axes:
## (Showing 8 of 21 eigenvalues)
## PCoA1 PCoA2 PCoA3 PCoA4 PCoA5 PCoA6 PCoA7 PCoA8
## 2.4339 1.1869 0.9571 0.4725 0.4027 0.3112 0.1809 0.1156
anova(m02)
## Analysis of Variance Table
##
## Response: Distances
## Df Sum Sq Mean Sq F value Pr(>F)
## Groups 1 0.00638 0.006378 0.1212 0.7313
## Residuals 20 1.05203 0.052602
permutest(m02,pairwise = TRUE, permutations = 99)
##
## Permutation test for homogeneity of multivariate dispersions
## Permutation: free
## Number of permutations: 99
##
## Response: Distances
## Df Sum Sq Mean Sq F N.Perm Pr(>F)
## Groups 1 0.00638 0.006378 0.1212 99 0.71
## Residuals 20 1.05203 0.052602
##
## Pairwise comparisons:
## (Observed p-value below diagonal, permuted p-value above diagonal)
## Density Open
## Density 0.7
## Open 0.73133
m02.HSD <- TukeyHSD(m02)
boxplot(m02)
m03 <- betadisper(dist, env$shrub_density)
m03
##
## Homogeneity of multivariate dispersions
##
## Call: betadisper(d = dist, group = env$shrub_density)
##
## No. of Positive Eigenvalues: 15
## No. of Negative Eigenvalues: 6
##
## Average distance to median:
## 0 10 11 12 13 14
## 0.4721 0.5171 0.4968 0.0000 0.0000 0.0000
##
## Eigenvalues for PCoA axes:
## (Showing 8 of 21 eigenvalues)
## PCoA1 PCoA2 PCoA3 PCoA4 PCoA5 PCoA6 PCoA7 PCoA8
## 2.4339 1.1869 0.9571 0.4725 0.4027 0.3112 0.1809 0.1156
anova(m03)
## Analysis of Variance Table
##
## Response: Distances
## Df Sum Sq Mean Sq F value Pr(>F)
## Groups 5 0.61999 0.12400 2.3507 0.08827 .
## Residuals 16 0.84400 0.05275
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
permutest(m03,pairwise = TRUE, permutations = 99)
##
## Permutation test for homogeneity of multivariate dispersions
## Permutation: free
## Number of permutations: 99
##
## Response: Distances
## Df Sum Sq Mean Sq F N.Perm Pr(>F)
## Groups 5 0.61999 0.12400 2.3507 99 0.06 .
## Residuals 16 0.84400 0.05275
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Pairwise comparisons:
## (Observed p-value below diagonal, permuted p-value above diagonal)
## 0 10 11 12 13 14
## 0 0.71000 0.85000
## 10 0.76968 0.87000
## 11 0.84659 0.89212
## 12
## 13
## 14
m03.HSD <- TukeyHSD(m03)
boxplot(m03)
m04 <- betadisper(dist, env$site)
m04
##
## Homogeneity of multivariate dispersions
##
## Call: betadisper(d = dist, group = env$site)
##
## No. of Positive Eigenvalues: 15
## No. of Negative Eigenvalues: 6
##
## Average distance to median:
## Carrizo Cuyama Tecopa
## 0.2828 0.3427 0.4625
##
## Eigenvalues for PCoA axes:
## (Showing 8 of 21 eigenvalues)
## PCoA1 PCoA2 PCoA3 PCoA4 PCoA5 PCoA6 PCoA7 PCoA8
## 2.4339 1.1869 0.9571 0.4725 0.4027 0.3112 0.1809 0.1156
anova(m04)
## Analysis of Variance Table
##
## Response: Distances
## Df Sum Sq Mean Sq F value Pr(>F)
## Groups 2 0.11217 0.056087 1.1941 0.3247
## Residuals 19 0.89240 0.046969
permutest(m04,pairwise = TRUE, permutations = 99)
##
## Permutation test for homogeneity of multivariate dispersions
## Permutation: free
## Number of permutations: 99
##
## Response: Distances
## Df Sum Sq Mean Sq F N.Perm Pr(>F)
## Groups 2 0.11217 0.056087 1.1941 99 0.29
## Residuals 19 0.89240 0.046969
##
## Pairwise comparisons:
## (Observed p-value below diagonal, permuted p-value above diagonal)
## Carrizo Cuyama Tecopa
## Carrizo 0.52000 0.15
## Cuyama 0.54695 0.34
## Tecopa 0.19772 0.31784
m04.HSD <- TukeyHSD(m04)
boxplot(m04)
### 2023 Data
photo_2023 <- read.csv("observations_2023.csv")
summary(photo_2023)
## region site site_code microsite
## Length:192701 Length:192701 Length:192701 Length:192701
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
## plot cam_ID month date
## Min. :1.000 Min. :1.00 Length:192701 Length:192701
## 1st Qu.:1.000 1st Qu.:1.00 Class :character Class :character
## Median :2.000 Median :1.00 Mode :character Mode :character
## Mean :1.771 Mean :1.49
## 3rd Qu.:2.000 3rd Qu.:2.00
## Max. :4.000 Max. :2.00
## year shrub_density rep identified_by
## Min. :2023 Min. : 0.000 Min. : 1 Length:192701
## 1st Qu.:2023 1st Qu.: 0.000 1st Qu.: 48176 Class :character
## Median :2023 Median :10.000 Median : 96351 Mode :character
## Mean :2023 Mean : 6.239 Mean : 96351
## 3rd Qu.:2023 3rd Qu.:11.000 3rd Qu.:144526
## Max. :2023 Max. :14.000 Max. :192701
## filename timestamp animal.hit class
## Length:192701 Length:192701 Min. :0.000000 Length:192701
## Class :character Class :character 1st Qu.:0.000000 Class :character
## Mode :character Mode :character Median :0.000000 Mode :character
## Mean :0.005392
## 3rd Qu.:0.000000
## Max. :1.000000
## order family genus species
## Length:192701 Length:192701 Length:192701 Length:192701
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
## common_name number_of_objects
## Length:192701 Min. :1
## Class :character 1st Qu.:1
## Mode :character Median :1
## Mean :1
## 3rd Qu.:1
## Max. :2
photo_2023 <- photo_2023 %>%
filter(common_name != "Human")
photo_2023 <- photo_2023 %>%
filter(common_name != "Human-Camera Trapper")
photo_2023 <- photo_2023 %>%
filter(common_name != "Domestic Dog")
photo_2023 <- photo_2023 %>%
filter(common_name != "Vehicle")
photo_2023 <- photo_2023 %>%
dplyr::filter(common_name != "Insect")
photo_2023 <- photo_2023 %>%
dplyr::filter(common_name != "Animal")
photo_2023 <- photo_2023 %>%
dplyr::filter(common_name != "Bird")
photo_2023 <- photo_2023 %>%
dplyr::filter(common_name != "No CV Result")
count.hit_2023 <- photo_2023 %>%
count(animal.hit) %>%
na.omit()
summary(count.hit_2023)
## animal.hit n
## Min. :0.00 Min. : 546
## 1st Qu.:0.25 1st Qu.: 48325
## Median :0.50 Median : 96104
## Mean :0.50 Mean : 96104
## 3rd Qu.:0.75 3rd Qu.:143883
## Max. :1.00 Max. :191662
### 2023 had a 0.28% capture rate
### Animal Observations by Site_Code
animals_by_sitecode_2023 <- photo_2023%>%
group_by(site_code, microsite, common_name) %>%
summarise(captures = sum(animal.hit), n = n())
## `summarise()` has grouped output by 'site_code', 'microsite'. You can override
## using the `.groups` argument.
animals_by_sitecode_2023 <- animals_by_sitecode_2023 %>%
filter(common_name != "Blank") %>% filter(common_name != "No CV Result")
### Animal observations by Site 2023
animals_by_site_2023 <- photo_2023 %>% group_by(site,microsite,common_name) %>% summarise(captures = sum(animal.hit))
## `summarise()` has grouped output by 'site', 'microsite'. You can override using
## the `.groups` argument.
animals_by_site_2023 <- animals_by_site_2023 %>% filter(common_name != "Blank") %>% filter(common_name != "No CV Result")
### Animal observations by Density
animals_by_density_2023 <- photo_2023 %>% group_by(microsite,common_name) %>% summarise(captures = sum(animal.hit))
## `summarise()` has grouped output by 'microsite'. You can override using the
## `.groups` argument.
animals_by_density_2023 <- animals_by_density_2023 %>% filter(common_name != "Blank") %>% filter(common_name != "No CV Result")
### Total Observations 2023
Total_Observations_2023 <- photo_2023 %>% group_by(common_name) %>% summarise(total = sum(animal.hit)) %>% filter(common_name != "Blank") %>% filter(common_name != "No CV Result")
density_obvs_2023 <- merge(animals_by_density_2023, Total_Observations_2023, all = TRUE)
density_obvs_2023$percent_presence <- density_obvs_2023$captures/density_obvs_2023$total
### Percent proportion Figure
plot2 <- ggplot(density_obvs_2023, aes(common_name, percent_presence, fill = microsite)) + geom_bar(stat = "identity") + coord_flip() + theme_classic() + scale_x_discrete(limits=rev) + xlab("Species") + ylab("Percent Proportion") + labs(fill = "Microsite")
plot2 + scale_fill_manual(values = c("#009900", "#0066cc"))
m2<- glm(total ~ microsite*common_name, family = "poisson", data = density_obvs_2023)
anova(m2, test = "Chisq")
## Analysis of Deviance Table
##
## Model: poisson, link: log
##
## Response: total
##
## Terms added sequentially (first to last)
##
##
## Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL 37 2413.4
## microsite 1 89.8 36 2323.6 <2e-16 ***
## common_name 24 2323.6 12 0.0 <2e-16 ***
## microsite:common_name 12 0.0 0 0.0 1
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
e2 <- emmeans(m2, pairwise~common_name)
## NOTE: Results may be misleading due to involvement in interactions
e2
## $emmeans
## common_name emmean SE df asymp.LCL asymp.UCL
## Black-tailed Jackrabbit 4.84 0.0630 Inf 4.713 4.96
## Black-throated Sparrow nonEst NA NA NA NA
## Blunt-nosed Leopard Lizard nonEst NA NA NA NA
## Brewer's Blackbird 1.61 0.3162 Inf 0.990 2.23
## California Ground Squirrel nonEst NA NA NA NA
## California Quail nonEst NA NA NA NA
## California Thrasher nonEst NA NA NA NA
## Common Raven 3.30 0.1361 Inf 3.029 3.56
## Coyote 3.81 0.1054 Inf 3.600 4.01
## Desert Cottontail nonEst NA NA NA NA
## Desert Iguana nonEst NA NA NA NA
## Giant Kangaroo Rat 2.20 0.2357 Inf 1.735 2.66
## Heermann's Kangaroo Rat 5.45 0.0464 Inf 5.356 5.54
## Horned Lark nonEst NA NA NA NA
## Kit Fox 1.95 0.2673 Inf 1.422 2.47
## Lizards and Snakes nonEst NA NA NA NA
## Loggerhead Shrike 1.61 0.3162 Inf 0.990 2.23
## Mammal nonEst NA NA NA NA
## Merriam's Kangaroo Rat 2.56 0.1961 Inf 2.181 2.95
## Nelson's Antelope Squirrel 3.37 0.1313 Inf 3.110 3.62
## Salinas Pocket Mouse 1.10 0.4082 Inf 0.298 1.90
## Say's Phoebe 1.39 0.3536 Inf 0.693 2.08
## Vesper Sparrow nonEst NA NA NA NA
## Western whiptail nonEst NA NA NA NA
## White-tailed Antelope Squirrel 1.79 0.2887 Inf 1.226 2.36
##
## Results are averaged over the levels of: microsite
## Results are given on the log (not the response) scale.
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate
## (Black-tailed Jackrabbit) - (Black-throated Sparrow) nonEst
## (Black-tailed Jackrabbit) - (Blunt-nosed Leopard Lizard) nonEst
## (Black-tailed Jackrabbit) - Brewer's Blackbird 3.2268
## (Black-tailed Jackrabbit) - California Ground Squirrel nonEst
## (Black-tailed Jackrabbit) - California Quail nonEst
## (Black-tailed Jackrabbit) - California Thrasher nonEst
## (Black-tailed Jackrabbit) - Common Raven 1.5404
## (Black-tailed Jackrabbit) - Coyote 1.0296
## (Black-tailed Jackrabbit) - Desert Cottontail nonEst
## (Black-tailed Jackrabbit) - Desert Iguana nonEst
## (Black-tailed Jackrabbit) - Giant Kangaroo Rat 2.6391
## (Black-tailed Jackrabbit) - Heermann's Kangaroo Rat -0.6105
## (Black-tailed Jackrabbit) - Horned Lark nonEst
## (Black-tailed Jackrabbit) - Kit Fox 2.8904
## (Black-tailed Jackrabbit) - Lizards and Snakes nonEst
## (Black-tailed Jackrabbit) - Loggerhead Shrike 3.2268
## (Black-tailed Jackrabbit) - Mammal nonEst
## (Black-tailed Jackrabbit) - Merriam's Kangaroo Rat 2.2713
## (Black-tailed Jackrabbit) - Nelson's Antelope Squirrel 1.4690
## (Black-tailed Jackrabbit) - Salinas Pocket Mouse 3.7377
## (Black-tailed Jackrabbit) - Say's Phoebe 3.4500
## (Black-tailed Jackrabbit) - Vesper Sparrow nonEst
## (Black-tailed Jackrabbit) - Western whiptail nonEst
## (Black-tailed Jackrabbit) - (White-tailed Antelope Squirrel) 3.0445
## (Black-throated Sparrow) - (Blunt-nosed Leopard Lizard) nonEst
## (Black-throated Sparrow) - Brewer's Blackbird nonEst
## (Black-throated Sparrow) - California Ground Squirrel nonEst
## (Black-throated Sparrow) - California Quail nonEst
## (Black-throated Sparrow) - California Thrasher nonEst
## (Black-throated Sparrow) - Common Raven nonEst
## (Black-throated Sparrow) - Coyote nonEst
## (Black-throated Sparrow) - Desert Cottontail nonEst
## (Black-throated Sparrow) - Desert Iguana nonEst
## (Black-throated Sparrow) - Giant Kangaroo Rat nonEst
## (Black-throated Sparrow) - Heermann's Kangaroo Rat nonEst
## (Black-throated Sparrow) - Horned Lark nonEst
## (Black-throated Sparrow) - Kit Fox nonEst
## (Black-throated Sparrow) - Lizards and Snakes nonEst
## (Black-throated Sparrow) - Loggerhead Shrike nonEst
## (Black-throated Sparrow) - Mammal nonEst
## (Black-throated Sparrow) - Merriam's Kangaroo Rat nonEst
## (Black-throated Sparrow) - Nelson's Antelope Squirrel nonEst
## (Black-throated Sparrow) - Salinas Pocket Mouse nonEst
## (Black-throated Sparrow) - Say's Phoebe nonEst
## (Black-throated Sparrow) - Vesper Sparrow nonEst
## (Black-throated Sparrow) - Western whiptail nonEst
## (Black-throated Sparrow) - (White-tailed Antelope Squirrel) nonEst
## (Blunt-nosed Leopard Lizard) - Brewer's Blackbird nonEst
## (Blunt-nosed Leopard Lizard) - California Ground Squirrel nonEst
## (Blunt-nosed Leopard Lizard) - California Quail nonEst
## (Blunt-nosed Leopard Lizard) - California Thrasher nonEst
## (Blunt-nosed Leopard Lizard) - Common Raven nonEst
## (Blunt-nosed Leopard Lizard) - Coyote nonEst
## (Blunt-nosed Leopard Lizard) - Desert Cottontail nonEst
## (Blunt-nosed Leopard Lizard) - Desert Iguana nonEst
## (Blunt-nosed Leopard Lizard) - Giant Kangaroo Rat nonEst
## (Blunt-nosed Leopard Lizard) - Heermann's Kangaroo Rat nonEst
## (Blunt-nosed Leopard Lizard) - Horned Lark nonEst
## (Blunt-nosed Leopard Lizard) - Kit Fox nonEst
## (Blunt-nosed Leopard Lizard) - Lizards and Snakes nonEst
## (Blunt-nosed Leopard Lizard) - Loggerhead Shrike nonEst
## (Blunt-nosed Leopard Lizard) - Mammal nonEst
## (Blunt-nosed Leopard Lizard) - Merriam's Kangaroo Rat nonEst
## (Blunt-nosed Leopard Lizard) - Nelson's Antelope Squirrel nonEst
## (Blunt-nosed Leopard Lizard) - Salinas Pocket Mouse nonEst
## (Blunt-nosed Leopard Lizard) - Say's Phoebe nonEst
## (Blunt-nosed Leopard Lizard) - Vesper Sparrow nonEst
## (Blunt-nosed Leopard Lizard) - Western whiptail nonEst
## (Blunt-nosed Leopard Lizard) - (White-tailed Antelope Squirrel) nonEst
## Brewer's Blackbird - California Ground Squirrel nonEst
## Brewer's Blackbird - California Quail nonEst
## Brewer's Blackbird - California Thrasher nonEst
## Brewer's Blackbird - Common Raven -1.6864
## Brewer's Blackbird - Coyote -2.1972
## Brewer's Blackbird - Desert Cottontail nonEst
## Brewer's Blackbird - Desert Iguana nonEst
## Brewer's Blackbird - Giant Kangaroo Rat -0.5878
## Brewer's Blackbird - Heermann's Kangaroo Rat -3.8373
## Brewer's Blackbird - Horned Lark nonEst
## Brewer's Blackbird - Kit Fox -0.3365
## Brewer's Blackbird - Lizards and Snakes nonEst
## Brewer's Blackbird - Loggerhead Shrike 0.0000
## Brewer's Blackbird - Mammal nonEst
## Brewer's Blackbird - Merriam's Kangaroo Rat -0.9555
## Brewer's Blackbird - Nelson's Antelope Squirrel -1.7579
## Brewer's Blackbird - Salinas Pocket Mouse 0.5108
## Brewer's Blackbird - Say's Phoebe 0.2231
## Brewer's Blackbird - Vesper Sparrow nonEst
## Brewer's Blackbird - Western whiptail nonEst
## Brewer's Blackbird - (White-tailed Antelope Squirrel) -0.1823
## California Ground Squirrel - California Quail nonEst
## California Ground Squirrel - California Thrasher nonEst
## California Ground Squirrel - Common Raven nonEst
## California Ground Squirrel - Coyote nonEst
## California Ground Squirrel - Desert Cottontail nonEst
## California Ground Squirrel - Desert Iguana nonEst
## California Ground Squirrel - Giant Kangaroo Rat nonEst
## California Ground Squirrel - Heermann's Kangaroo Rat nonEst
## California Ground Squirrel - Horned Lark nonEst
## California Ground Squirrel - Kit Fox nonEst
## California Ground Squirrel - Lizards and Snakes nonEst
## California Ground Squirrel - Loggerhead Shrike nonEst
## California Ground Squirrel - Mammal nonEst
## California Ground Squirrel - Merriam's Kangaroo Rat nonEst
## California Ground Squirrel - Nelson's Antelope Squirrel nonEst
## California Ground Squirrel - Salinas Pocket Mouse nonEst
## California Ground Squirrel - Say's Phoebe nonEst
## California Ground Squirrel - Vesper Sparrow nonEst
## California Ground Squirrel - Western whiptail nonEst
## California Ground Squirrel - (White-tailed Antelope Squirrel) nonEst
## California Quail - California Thrasher nonEst
## California Quail - Common Raven nonEst
## California Quail - Coyote nonEst
## California Quail - Desert Cottontail nonEst
## California Quail - Desert Iguana nonEst
## California Quail - Giant Kangaroo Rat nonEst
## California Quail - Heermann's Kangaroo Rat nonEst
## California Quail - Horned Lark nonEst
## California Quail - Kit Fox nonEst
## California Quail - Lizards and Snakes nonEst
## California Quail - Loggerhead Shrike nonEst
## California Quail - Mammal nonEst
## California Quail - Merriam's Kangaroo Rat nonEst
## California Quail - Nelson's Antelope Squirrel nonEst
## California Quail - Salinas Pocket Mouse nonEst
## California Quail - Say's Phoebe nonEst
## California Quail - Vesper Sparrow nonEst
## California Quail - Western whiptail nonEst
## California Quail - (White-tailed Antelope Squirrel) nonEst
## California Thrasher - Common Raven nonEst
## California Thrasher - Coyote nonEst
## California Thrasher - Desert Cottontail nonEst
## California Thrasher - Desert Iguana nonEst
## California Thrasher - Giant Kangaroo Rat nonEst
## California Thrasher - Heermann's Kangaroo Rat nonEst
## California Thrasher - Horned Lark nonEst
## California Thrasher - Kit Fox nonEst
## California Thrasher - Lizards and Snakes nonEst
## California Thrasher - Loggerhead Shrike nonEst
## California Thrasher - Mammal nonEst
## California Thrasher - Merriam's Kangaroo Rat nonEst
## California Thrasher - Nelson's Antelope Squirrel nonEst
## California Thrasher - Salinas Pocket Mouse nonEst
## California Thrasher - Say's Phoebe nonEst
## California Thrasher - Vesper Sparrow nonEst
## California Thrasher - Western whiptail nonEst
## California Thrasher - (White-tailed Antelope Squirrel) nonEst
## Common Raven - Coyote -0.5108
## Common Raven - Desert Cottontail nonEst
## Common Raven - Desert Iguana nonEst
## Common Raven - Giant Kangaroo Rat 1.0986
## Common Raven - Heermann's Kangaroo Rat -2.1509
## Common Raven - Horned Lark nonEst
## Common Raven - Kit Fox 1.3499
## Common Raven - Lizards and Snakes nonEst
## Common Raven - Loggerhead Shrike 1.6864
## Common Raven - Mammal nonEst
## Common Raven - Merriam's Kangaroo Rat 0.7309
## Common Raven - Nelson's Antelope Squirrel -0.0715
## Common Raven - Salinas Pocket Mouse 2.1972
## Common Raven - Say's Phoebe 1.9095
## Common Raven - Vesper Sparrow nonEst
## Common Raven - Western whiptail nonEst
## Common Raven - (White-tailed Antelope Squirrel) 1.5041
## Coyote - Desert Cottontail nonEst
## Coyote - Desert Iguana nonEst
## Coyote - Giant Kangaroo Rat 1.6094
## Coyote - Heermann's Kangaroo Rat -1.6401
## Coyote - Horned Lark nonEst
## Coyote - Kit Fox 1.8608
## Coyote - Lizards and Snakes nonEst
## Coyote - Loggerhead Shrike 2.1972
## Coyote - Mammal nonEst
## Coyote - Merriam's Kangaroo Rat 1.2417
## Coyote - Nelson's Antelope Squirrel 0.4394
## Coyote - Salinas Pocket Mouse 2.7081
## Coyote - Say's Phoebe 2.4204
## Coyote - Vesper Sparrow nonEst
## Coyote - Western whiptail nonEst
## Coyote - (White-tailed Antelope Squirrel) 2.0149
## Desert Cottontail - Desert Iguana nonEst
## Desert Cottontail - Giant Kangaroo Rat nonEst
## Desert Cottontail - Heermann's Kangaroo Rat nonEst
## Desert Cottontail - Horned Lark nonEst
## Desert Cottontail - Kit Fox nonEst
## Desert Cottontail - Lizards and Snakes nonEst
## Desert Cottontail - Loggerhead Shrike nonEst
## Desert Cottontail - Mammal nonEst
## Desert Cottontail - Merriam's Kangaroo Rat nonEst
## Desert Cottontail - Nelson's Antelope Squirrel nonEst
## Desert Cottontail - Salinas Pocket Mouse nonEst
## Desert Cottontail - Say's Phoebe nonEst
## Desert Cottontail - Vesper Sparrow nonEst
## Desert Cottontail - Western whiptail nonEst
## Desert Cottontail - (White-tailed Antelope Squirrel) nonEst
## Desert Iguana - Giant Kangaroo Rat nonEst
## Desert Iguana - Heermann's Kangaroo Rat nonEst
## Desert Iguana - Horned Lark nonEst
## Desert Iguana - Kit Fox nonEst
## Desert Iguana - Lizards and Snakes nonEst
## Desert Iguana - Loggerhead Shrike nonEst
## Desert Iguana - Mammal nonEst
## Desert Iguana - Merriam's Kangaroo Rat nonEst
## Desert Iguana - Nelson's Antelope Squirrel nonEst
## Desert Iguana - Salinas Pocket Mouse nonEst
## Desert Iguana - Say's Phoebe nonEst
## Desert Iguana - Vesper Sparrow nonEst
## Desert Iguana - Western whiptail nonEst
## Desert Iguana - (White-tailed Antelope Squirrel) nonEst
## Giant Kangaroo Rat - Heermann's Kangaroo Rat -3.2495
## Giant Kangaroo Rat - Horned Lark nonEst
## Giant Kangaroo Rat - Kit Fox 0.2513
## Giant Kangaroo Rat - Lizards and Snakes nonEst
## Giant Kangaroo Rat - Loggerhead Shrike 0.5878
## Giant Kangaroo Rat - Mammal nonEst
## Giant Kangaroo Rat - Merriam's Kangaroo Rat -0.3677
## Giant Kangaroo Rat - Nelson's Antelope Squirrel -1.1701
## Giant Kangaroo Rat - Salinas Pocket Mouse 1.0986
## Giant Kangaroo Rat - Say's Phoebe 0.8109
## Giant Kangaroo Rat - Vesper Sparrow nonEst
## Giant Kangaroo Rat - Western whiptail nonEst
## Giant Kangaroo Rat - (White-tailed Antelope Squirrel) 0.4055
## Heermann's Kangaroo Rat - Horned Lark nonEst
## Heermann's Kangaroo Rat - Kit Fox 3.5008
## Heermann's Kangaroo Rat - Lizards and Snakes nonEst
## Heermann's Kangaroo Rat - Loggerhead Shrike 3.8373
## Heermann's Kangaroo Rat - Mammal nonEst
## Heermann's Kangaroo Rat - Merriam's Kangaroo Rat 2.8818
## Heermann's Kangaroo Rat - Nelson's Antelope Squirrel 2.0794
## Heermann's Kangaroo Rat - Salinas Pocket Mouse 4.3481
## Heermann's Kangaroo Rat - Say's Phoebe 4.0604
## Heermann's Kangaroo Rat - Vesper Sparrow nonEst
## Heermann's Kangaroo Rat - Western whiptail nonEst
## Heermann's Kangaroo Rat - (White-tailed Antelope Squirrel) 3.6550
## Horned Lark - Kit Fox nonEst
## Horned Lark - Lizards and Snakes nonEst
## Horned Lark - Loggerhead Shrike nonEst
## Horned Lark - Mammal nonEst
## Horned Lark - Merriam's Kangaroo Rat nonEst
## Horned Lark - Nelson's Antelope Squirrel nonEst
## Horned Lark - Salinas Pocket Mouse nonEst
## Horned Lark - Say's Phoebe nonEst
## Horned Lark - Vesper Sparrow nonEst
## Horned Lark - Western whiptail nonEst
## Horned Lark - (White-tailed Antelope Squirrel) nonEst
## Kit Fox - Lizards and Snakes nonEst
## Kit Fox - Loggerhead Shrike 0.3365
## Kit Fox - Mammal nonEst
## Kit Fox - Merriam's Kangaroo Rat -0.6190
## Kit Fox - Nelson's Antelope Squirrel -1.4214
## Kit Fox - Salinas Pocket Mouse 0.8473
## Kit Fox - Say's Phoebe 0.5596
## Kit Fox - Vesper Sparrow nonEst
## Kit Fox - Western whiptail nonEst
## Kit Fox - (White-tailed Antelope Squirrel) 0.1542
## Lizards and Snakes - Loggerhead Shrike nonEst
## Lizards and Snakes - Mammal nonEst
## Lizards and Snakes - Merriam's Kangaroo Rat nonEst
## Lizards and Snakes - Nelson's Antelope Squirrel nonEst
## Lizards and Snakes - Salinas Pocket Mouse nonEst
## Lizards and Snakes - Say's Phoebe nonEst
## Lizards and Snakes - Vesper Sparrow nonEst
## Lizards and Snakes - Western whiptail nonEst
## Lizards and Snakes - (White-tailed Antelope Squirrel) nonEst
## Loggerhead Shrike - Mammal nonEst
## Loggerhead Shrike - Merriam's Kangaroo Rat -0.9555
## Loggerhead Shrike - Nelson's Antelope Squirrel -1.7579
## Loggerhead Shrike - Salinas Pocket Mouse 0.5108
## Loggerhead Shrike - Say's Phoebe 0.2231
## Loggerhead Shrike - Vesper Sparrow nonEst
## Loggerhead Shrike - Western whiptail nonEst
## Loggerhead Shrike - (White-tailed Antelope Squirrel) -0.1823
## Mammal - Merriam's Kangaroo Rat nonEst
## Mammal - Nelson's Antelope Squirrel nonEst
## Mammal - Salinas Pocket Mouse nonEst
## Mammal - Say's Phoebe nonEst
## Mammal - Vesper Sparrow nonEst
## Mammal - Western whiptail nonEst
## Mammal - (White-tailed Antelope Squirrel) nonEst
## Merriam's Kangaroo Rat - Nelson's Antelope Squirrel -0.8023
## Merriam's Kangaroo Rat - Salinas Pocket Mouse 1.4663
## Merriam's Kangaroo Rat - Say's Phoebe 1.1787
## Merriam's Kangaroo Rat - Vesper Sparrow nonEst
## Merriam's Kangaroo Rat - Western whiptail nonEst
## Merriam's Kangaroo Rat - (White-tailed Antelope Squirrel) 0.7732
## Nelson's Antelope Squirrel - Salinas Pocket Mouse 2.2687
## Nelson's Antelope Squirrel - Say's Phoebe 1.9810
## Nelson's Antelope Squirrel - Vesper Sparrow nonEst
## Nelson's Antelope Squirrel - Western whiptail nonEst
## Nelson's Antelope Squirrel - (White-tailed Antelope Squirrel) 1.5755
## Salinas Pocket Mouse - Say's Phoebe -0.2877
## Salinas Pocket Mouse - Vesper Sparrow nonEst
## Salinas Pocket Mouse - Western whiptail nonEst
## Salinas Pocket Mouse - (White-tailed Antelope Squirrel) -0.6931
## Say's Phoebe - Vesper Sparrow nonEst
## Say's Phoebe - Western whiptail nonEst
## Say's Phoebe - (White-tailed Antelope Squirrel) -0.4055
## Vesper Sparrow - Western whiptail nonEst
## Vesper Sparrow - (White-tailed Antelope Squirrel) nonEst
## Western whiptail - (White-tailed Antelope Squirrel) nonEst
## SE df z.ratio p.value
## NA NA NA NA
## NA NA NA NA
## 0.3224 Inf 10.008 <.0001
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## 0.1500 Inf 10.273 <.0001
## 0.1228 Inf 8.385 <.0001
## NA NA NA NA
## NA NA NA NA
## 0.2440 Inf 10.817 <.0001
## 0.0783 Inf -7.801 <.0001
## NA NA NA NA
## 0.2746 Inf 10.526 <.0001
## NA NA NA NA
## 0.3224 Inf 10.008 <.0001
## NA NA NA NA
## 0.2060 Inf 11.027 <.0001
## 0.1456 Inf 10.087 <.0001
## 0.4131 Inf 9.048 <.0001
## 0.3591 Inf 9.607 <.0001
## NA NA NA NA
## NA NA NA NA
## 0.2955 Inf 10.304 <.0001
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## 0.3443 Inf -4.899 0.0003
## 0.3333 Inf -6.592 <.0001
## NA NA NA NA
## NA NA NA NA
## 0.3944 Inf -1.490 0.9991
## 0.3196 Inf -12.006 <.0001
## NA NA NA NA
## 0.4140 Inf -0.813 1.0000
## NA NA NA NA
## 0.4472 Inf 0.000 1.0000
## NA NA NA NA
## 0.3721 Inf -2.568 0.6434
## 0.3424 Inf -5.134 0.0001
## 0.5164 Inf 0.989 1.0000
## 0.4743 Inf 0.470 1.0000
## NA NA NA NA
## NA NA NA NA
## 0.4282 Inf -0.426 1.0000
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## 0.1721 Inf -2.968 0.3313
## NA NA NA NA
## NA NA NA NA
## 0.2722 Inf 4.037 0.0127
## 0.1438 Inf -14.959 <.0001
## NA NA NA NA
## 0.2999 Inf 4.501 0.0018
## NA NA NA NA
## 0.3443 Inf 4.899 0.0003
## NA NA NA NA
## 0.2387 Inf 3.062 0.2698
## 0.1891 Inf -0.378 1.0000
## 0.4303 Inf 5.106 0.0001
## 0.3788 Inf 5.041 0.0001
## NA NA NA NA
## NA NA NA NA
## 0.3191 Inf 4.713 0.0007
## NA NA NA NA
## NA NA NA NA
## 0.2582 Inf 6.233 <.0001
## 0.1152 Inf -14.239 <.0001
## NA NA NA NA
## 0.2873 Inf 6.477 <.0001
## NA NA NA NA
## 0.3333 Inf 6.592 <.0001
## NA NA NA NA
## 0.2226 Inf 5.577 <.0001
## 0.1684 Inf 2.609 0.6102
## 0.4216 Inf 6.423 <.0001
## 0.3689 Inf 6.560 <.0001
## NA NA NA NA
## NA NA NA NA
## 0.3073 Inf 6.556 <.0001
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## 0.2402 Inf -13.527 <.0001
## NA NA NA NA
## 0.3563 Inf 0.705 1.0000
## NA NA NA NA
## 0.3944 Inf 1.490 0.9991
## NA NA NA NA
## 0.3066 Inf -1.199 1.0000
## 0.2698 Inf -4.337 0.0037
## 0.4714 Inf 2.331 0.8137
## 0.4249 Inf 1.908 0.9731
## NA NA NA NA
## NA NA NA NA
## 0.3727 Inf 1.088 1.0000
## NA NA NA NA
## 0.2713 Inf 12.906 <.0001
## NA NA NA NA
## 0.3196 Inf 12.006 <.0001
## NA NA NA NA
## 0.2015 Inf 14.299 <.0001
## 0.1393 Inf 14.931 <.0001
## 0.4109 Inf 10.582 <.0001
## 0.3566 Inf 11.387 <.0001
## NA NA NA NA
## NA NA NA NA
## 0.2924 Inf 12.501 <.0001
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## 0.4140 Inf 0.813 1.0000
## NA NA NA NA
## 0.3315 Inf -1.867 0.9792
## 0.2978 Inf -4.773 0.0005
## 0.4879 Inf 1.736 0.9917
## 0.4432 Inf 1.263 0.9999
## NA NA NA NA
## NA NA NA NA
## 0.3934 Inf 0.392 1.0000
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## 0.3721 Inf -2.568 0.6434
## 0.3424 Inf -5.134 0.0001
## 0.5164 Inf 0.989 1.0000
## 0.4743 Inf 0.470 1.0000
## NA NA NA NA
## NA NA NA NA
## 0.4282 Inf -0.426 1.0000
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## 0.2360 Inf -3.400 0.1122
## 0.4529 Inf 3.238 0.1755
## 0.4043 Inf 2.915 0.3684
## NA NA NA NA
## NA NA NA NA
## 0.3490 Inf 2.216 0.8770
## 0.4288 Inf 5.290 <.0001
## 0.3771 Inf 5.253 <.0001
## NA NA NA NA
## NA NA NA NA
## 0.3171 Inf 4.968 0.0002
## 0.5401 Inf -0.533 1.0000
## NA NA NA NA
## NA NA NA NA
## 0.5000 Inf -1.386 0.9997
## NA NA NA NA
## NA NA NA NA
## 0.4564 Inf -0.888 1.0000
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
##
## Results are averaged over the levels of: microsite
## Results are given on the log (not the response) scale.
## P value adjustment: tukey method for comparing a family of 25 estimates
animals_density_2023 <- photo_2023 %>% group_by(site_code,microsite,plot, shrub_density, common_name) %>% summarise(captures = sum(animal.hit))
## `summarise()` has grouped output by 'site_code', 'microsite', 'plot',
## 'shrub_density'. You can override using the `.groups` argument.
animals_density_2023 <- animals_density_2023 %>% filter(common_name != "Blank")
pca_data_2023 <- animals_density_2023 ### Created new df for pcoa data
pca_data_2023 <- pca_data_2023 %>%
spread(common_name, captures) %>%
ungroup() %>%
dplyr::select(-site_code, -microsite, -plot) %>%
replace(is.na(.),0)
dim(pca_data_2023)
## [1] 23 26
env_2023 <- read.csv("environment_2023.csv") ### Drop Tecopa open 1, Tecopa open 4, since they have no animal observations.
dim(env)
## [1] 22 5
model01 <- adonis(pca_data_2023 ~ microsite*shrub_density, data = env_2023)
## 'adonis' will be deprecated: use 'adonis2' instead
model01
## $aov.tab
## Permutation: free
## Number of permutations: 999
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## microsite 1 0.4511 0.45114 1.6082 0.06930 0.150
## shrub_density 1 0.4482 0.44823 1.5978 0.06885 0.151
## Residuals 20 5.6105 0.28052 0.86184
## Total 22 6.5099 1.00000
##
## $call
## adonis(formula = pca_data_2023 ~ microsite * shrub_density, data = env_2023)
##
## $coefficients
## shrub_density Black-tailed Jackrabbit
## (Intercept) -10.398601 2.8438228
## microsite1 -14.216783 -3.7925408
## shrub_density 2.876923 0.4769231
## microsite1:shrub_density NA NA
## Black-throated Sparrow Blunt-nosed Leopard Lizard
## (Intercept) 4.545455e-02 0.12820513
## microsite1 -4.545455e-02 0.12820513
## shrub_density -6.331379e-18 -0.01538462
## microsite1:shrub_density NA NA
## Brewer's Blackbird California Ground Squirrel
## (Intercept) -2.5571096 -0.21794872
## microsite1 -2.6480186 -0.21794872
## shrub_density 0.4923077 0.04615385
## microsite1:shrub_density NA NA
## California Quail California Thrasher Common Raven
## (Intercept) -1.8205128 0.47435897 2.6340326
## microsite1 -1.8205128 0.47435897 1.7249417
## shrub_density 0.3384615 -0.07692308 -0.2615385
## microsite1:shrub_density NA NA NA
## Coyote Desert Cottontail Desert Iguana
## (Intercept) 3.060606 -2.3391608 4.545455e-02
## microsite1 1.606061 -2.4300699 -4.545455e-02
## shrub_density -0.200000 0.4461538 8.356568e-18
## microsite1:shrub_density NA NA NA
## Giant Kangaroo Rat Heermann's Kangaroo Rat Horned Lark
## (Intercept) 0.03263403 -7.849650 0.38461538
## microsite1 -0.05827506 -15.304196 0.38461538
## shrub_density 0.06153846 3.169231 -0.04615385
## microsite1:shrub_density NA NA NA
## Kit Fox Lizards and Snakes Loggerhead Shrike
## (Intercept) 2.1165501 2.727273e-01 -0.4801865
## microsite1 1.9347319 -2.727273e-01 -0.5710956
## shrub_density -0.3230769 -1.513076e-17 0.1230769
## microsite1:shrub_density NA NA NA
## Mammal Merriam's Kangaroo Rat
## (Intercept) -0.21794872 1.10256410
## microsite1 -0.21794872 0.10256410
## shrub_density 0.04615385 -0.09230769
## microsite1:shrub_density NA NA
## Nelson's Antelope Squirrel Salinas Pocket Mouse
## (Intercept) 0.3694639 -0.39044289
## microsite1 -0.2668998 -0.48135198
## shrub_density 0.1538462 0.09230769
## microsite1:shrub_density NA NA
## Say's Phoebe Vesper Sparrow Western whiptail
## (Intercept) 0.264568765 1.5384615 0.12820513
## microsite1 -0.008158508 1.5384615 0.12820513
## shrub_density -0.015384615 -0.1846154 -0.01538462
## microsite1:shrub_density NA NA NA
## White-tailed Antelope Squirrel
## (Intercept) 0.35547786
## microsite1 -0.09906760
## shrub_density -0.01538462
## microsite1:shrub_density NA
##
## $coef.sites
## 1 2 3 4
## (Intercept) 1.08713373 1.19242360 1.04021622 0.53368864
## microsite1 0.27702067 0.36812565 0.28650205 -0.08921598
## shrub_density -0.05750503 -0.07418322 -0.03323392 0.02592824
## microsite1:shrub_density NA NA NA NA
## 5 6 7 8
## (Intercept) 1.09169046 0.725232927 0.9149049 0.73036046
## microsite1 0.43517627 -0.003397368 0.1285966 -0.03557956
## shrub_density -0.08696283 -0.012927632 -0.0247195 0.01666357
## microsite1:shrub_density NA NA NA NA
## 9 10 11 12
## (Intercept) 1.3003752 1.4998669 0.80294716 0.64129703
## microsite1 0.6190745 0.7936762 0.05159731 -0.03324735
## shrub_density -0.1227435 -0.1491194 -0.01154984 0.01898538
## microsite1:shrub_density NA NA NA NA
## 13 14 15 16
## (Intercept) 0.99239018 0.66809163 0.63858697 -0.08681298
## microsite1 0.34039631 -0.04876636 -0.21003546 -0.87304773
## shrub_density -0.06950611 -0.01035118 0.04254088 0.14042524
## microsite1:shrub_density NA NA NA NA
## 17 18 19 20
## (Intercept) 0.17811261 0.1694050 -0.01538676 0.91285079
## microsite1 -0.64882951 -0.6891926 -0.81989153 0.22319732
## shrub_density 0.09926822 0.1072032 0.13358216 -0.04137692
## microsite1:shrub_density NA NA NA NA
## 21 22 23
## (Intercept) 0.95345668 1.13091299 0.9134932
## microsite1 0.30456994 0.48161788 0.2756840
## shrub_density -0.05633216 -0.08893335 -0.0522899
## microsite1:shrub_density NA NA NA
##
## $f.perms
## [,1] [,2]
## [1,] 0.46530682 1.02723751
## [2,] 0.88426933 0.78604776
## [3,] 0.70620154 0.63080175
## [4,] 0.78452045 0.95628203
## [5,] 0.65625235 1.82310508
## [6,] 0.41204149 0.54038847
## [7,] 0.89333851 1.00396992
## [8,] 0.41256690 1.48955056
## [9,] 0.48282021 1.02010885
## [10,] 1.14187511 2.00075243
## [11,] 0.36229674 1.56766319
## [12,] 0.58029315 0.42205591
## [13,] 0.58982293 1.60395896
## [14,] 0.40710734 0.77769454
## [15,] 0.95333565 1.88883713
## [16,] 1.89469529 1.59088448
## [17,] 2.45304109 0.63421638
## [18,] 0.69630316 1.34923615
## [19,] 0.80457920 0.77456754
## [20,] 0.15063732 0.69008846
## [21,] 1.13696558 0.42212376
## [22,] 0.57269079 0.84454886
## [23,] 1.18934103 1.11533190
## [24,] 0.83650477 0.90631662
## [25,] 0.57862928 1.42057363
## [26,] 0.61307193 0.62928553
## [27,] 0.50921759 1.41689070
## [28,] 0.60546656 0.64746795
## [29,] 1.25270446 0.48835053
## [30,] 1.26307206 2.38667203
## [31,] 0.23235340 1.61597370
## [32,] 0.25519945 0.33154146
## [33,] 0.75109231 0.42832222
## [34,] 1.06972304 0.71389876
## [35,] 1.07312335 1.24024031
## [36,] 0.83765182 1.14596365
## [37,] 0.23608720 1.38144786
## [38,] 0.23646657 0.48009064
## [39,] 0.58964491 0.37062061
## [40,] 0.48456003 0.90619211
## [41,] 0.65961997 1.24739487
## [42,] 1.25104421 1.45741741
## [43,] 0.56564461 0.54844614
## [44,] 0.25032223 0.77210011
## [45,] 0.95109709 1.22318596
## [46,] 1.46420310 1.11263496
## [47,] 0.51714262 0.62612509
## [48,] 2.76190601 0.70446752
## [49,] 1.18936147 0.16901212
## [50,] 1.64382681 0.48552713
## [51,] 3.23377952 1.82092259
## [52,] 0.24945196 1.69121480
## [53,] 1.27956261 0.95120022
## [54,] 0.96948035 1.08715516
## [55,] 1.59823567 1.08603772
## [56,] 1.04950411 0.91346544
## [57,] 1.01897396 0.45499685
## [58,] 1.15766496 0.78100845
## [59,] 1.01565455 0.69805486
## [60,] 2.31711393 0.72780980
## [61,] 0.44804372 1.71442977
## [62,] 0.79397276 0.69967097
## [63,] 1.39942111 1.26609972
## [64,] 1.68970019 1.23760441
## [65,] 0.91464806 1.36589037
## [66,] 2.53903862 1.60905130
## [67,] 0.61575439 0.49280490
## [68,] 0.41482495 2.18819519
## [69,] 0.83117127 0.46837486
## [70,] 0.76577886 2.13667526
## [71,] 0.51625655 0.48058234
## [72,] 1.46860717 1.05988007
## [73,] 0.30255506 0.40426702
## [74,] 1.29760873 1.56555964
## [75,] 1.41998330 3.24075853
## [76,] 1.24260780 0.92310513
## [77,] 1.80652569 2.14665450
## [78,] 1.91744283 0.85202856
## [79,] 1.43946643 0.77570236
## [80,] 0.77806136 0.51128660
## [81,] 0.71479324 0.73011698
## [82,] 0.53680056 0.85101026
## [83,] 1.07005151 1.11684791
## [84,] 1.71588906 1.34747867
## [85,] 0.76941961 1.16405303
## [86,] 0.63630776 0.91394931
## [87,] 0.91248219 0.98393008
## [88,] 1.00228284 0.67300761
## [89,] 0.65131253 1.75229157
## [90,] 0.77968516 1.52923439
## [91,] 1.71683735 1.69846664
## [92,] 0.61588691 1.52385656
## [93,] 1.05041722 1.15447018
## [94,] 0.93976045 0.70670096
## [95,] 0.64232490 0.82854646
## [96,] 0.70109004 0.53484380
## [97,] 1.04227843 1.28127132
## [98,] 0.62201413 1.04581002
## [99,] 0.36807410 0.87355216
## [100,] 1.24064707 1.27973479
## [101,] 0.36739973 2.13175783
## [102,] 0.48306130 0.95525639
## [103,] 0.81294670 1.34078557
## [104,] 0.95304735 1.73826827
## [105,] 0.86857800 1.21959915
## [106,] 0.40632261 0.69965638
## [107,] 0.86728796 0.58104252
## [108,] 1.41292956 1.05578068
## [109,] 0.53976593 0.41886274
## [110,] 0.91660335 1.50396749
## [111,] 1.89117847 0.99368072
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## [565,] 1.67341991 1.01839761
## [566,] 1.12321911 1.47345408
## [567,] 0.40089470 1.23441487
## [568,] 0.63769485 2.09147858
## [569,] 1.24486533 1.12220566
## [570,] 0.78564579 1.08061702
## [571,] 2.22935584 0.28598602
## [572,] 1.90208629 2.16702425
## [573,] 1.66935618 1.71889417
## [574,] 1.19900764 1.19051768
## [575,] 0.62766805 0.78524603
## [576,] 1.28445853 0.78980169
## [577,] 1.86484644 2.34963179
## [578,] 1.03829249 1.59728215
## [579,] 0.63671384 0.75044895
## [580,] 2.50292491 0.93780208
## [581,] 0.37516371 0.59890162
## [582,] 0.48996392 1.52667970
## [583,] 0.84307879 1.18399962
## [584,] 0.75631322 0.80190642
## [585,] 1.16827988 0.73508122
## [586,] 2.27347642 1.07754478
## [587,] 0.89686664 1.83090309
## [588,] 0.74283516 0.96726170
## [589,] 1.16454632 1.33286414
## [590,] 1.24404793 1.44060689
## [591,] 1.60977098 1.78888108
## [592,] 0.99131616 0.63625552
## [593,] 0.98073238 0.66520460
## [594,] 1.01679951 1.43951787
## [595,] 1.00433221 1.19171935
## [596,] 0.49212137 0.59876398
## [597,] 0.72732049 0.30557612
## [598,] 0.71362952 0.69008147
## [599,] 1.11159525 0.34646844
## [600,] 2.00884113 1.65720907
## [601,] 0.62772671 0.91317700
## [602,] 1.30121045 1.69172382
## [603,] 0.71036415 1.26388622
## [604,] 1.27706045 0.45624611
## [605,] 2.22044770 0.59001624
## [606,] 1.51558860 1.06470252
## [607,] 0.47803283 0.40454637
## [608,] 2.19996700 2.05975168
## [609,] 1.80480165 0.05907415
## [610,] 2.08356367 1.20237091
## [611,] 1.59582933 1.68143149
## [612,] 0.47271249 0.95826757
## [613,] 0.40382286 0.55853308
## [614,] 1.01307912 2.18091574
## [615,] 0.60233015 0.79918225
## [616,] 1.09009265 0.54330031
## [617,] 1.81616384 1.51231752
## [618,] 1.47209049 1.28719389
## [619,] 0.59686268 0.24376475
## [620,] 0.71795933 0.71062170
## [621,] 0.45170542 1.00393586
## [622,] 0.51719015 1.82516019
## [623,] 1.26477670 0.80437741
## [624,] 1.59283420 1.41972140
## [625,] 0.25930215 0.55027087
## [626,] 1.23112616 0.76449845
## [627,] 0.19529858 0.33440116
## [628,] 0.91177346 2.28568696
## [629,] 0.68974129 0.71167988
## [630,] 0.78595614 0.42168081
## [631,] 0.76992509 1.26912726
## [632,] 1.29915270 0.56109083
## [633,] 0.20474651 1.03397507
## [634,] 1.27267828 0.80808972
## [635,] 1.81401352 1.09141129
## [636,] 0.82354846 1.03765844
## [637,] 0.93069278 1.17030052
## [638,] 0.54519290 0.36851374
## [639,] 0.64603993 0.77543366
## [640,] 0.88082500 0.20232891
## [641,] 0.79855622 1.01308145
## [642,] 1.14376266 2.00900053
## [643,] 0.47705972 0.48297703
## [644,] 1.45388143 1.03062923
## [645,] 0.79224936 1.22357240
## [646,] 0.62502358 1.00599605
## [647,] 2.13153701 1.60434553
## [648,] 1.66196902 0.58885346
## [649,] 0.74242778 1.68312606
## [650,] 0.90770363 0.70970127
## [651,] 2.05902837 1.00935014
## [652,] 1.31594481 0.44081037
## [653,] 1.51873454 0.40077612
## [654,] 0.56920896 1.01841410
## [655,] 0.96682206 0.90113297
## [656,] 0.61813417 0.68985043
## [657,] 1.43788196 2.30596089
## [658,] 1.38351428 0.80354737
## [659,] 0.97140525 0.89179752
## [660,] 1.09958776 0.47628500
## [661,] 1.85878410 0.68967588
## [662,] 1.78831839 0.21004039
## [663,] 0.19578523 0.67381809
## [664,] 0.73318758 0.40480387
## [665,] 2.59211359 0.24161558
## [666,] 0.59893711 1.62265649
## [667,] 0.85179708 0.92419481
## [668,] 0.24411332 0.30596091
## [669,] 1.31311636 1.52722177
## [670,] 1.10997389 0.23736345
## [671,] 1.06878124 0.97423772
## [672,] 1.09009709 1.67234844
## [673,] 0.49896324 0.64922086
## [674,] 0.81276096 1.89428970
## [675,] 1.26611323 0.42244865
## [676,] 0.98759782 1.16934923
## [677,] 0.84542633 0.55506035
## [678,] 1.59116691 0.77960486
## [679,] 0.74183314 0.66936736
## [680,] 0.73715435 1.17574445
## [681,] 0.39988470 0.71445234
## [682,] 0.40765885 0.51412389
## [683,] 0.68484366 1.19166494
## [684,] 0.40929450 0.47773048
## [685,] 1.54431991 1.12294344
## [686,] 1.29634746 0.63095202
## [687,] 0.65242003 0.64499235
## [688,] 0.84655772 0.71860441
## [689,] 0.65887210 1.28227541
## [690,] 0.59879224 0.86005096
## [691,] 0.51365916 1.37011153
## [692,] 0.31277240 1.40899868
## [693,] 1.33742112 0.46207699
## [694,] 1.79956224 1.09422541
## [695,] 0.90063815 1.14054657
## [696,] 1.92610451 2.76604802
## [697,] 1.09862636 1.15894212
## [698,] 0.80525351 1.96218430
## [699,] 0.69888540 0.73513398
## [700,] 0.74703455 0.34379657
## [701,] 0.82878274 0.50914062
## [702,] 1.34772119 0.41788635
## [703,] 2.05977059 0.37884983
## [704,] 0.93779800 1.48301280
## [705,] 1.74010724 0.68327549
## [706,] 1.32456916 0.47980830
## [707,] 0.42162042 1.32521920
## [708,] 0.79797258 0.38981945
## [709,] 0.67769598 1.31225224
## [710,] 0.28193480 1.45015399
## [711,] 0.82540986 1.10354493
## [712,] 0.52794307 1.18293619
## [713,] 1.15669221 0.92652165
## [714,] 0.91824252 2.46954645
## [715,] 1.50684802 0.55075907
## [716,] 0.29308527 0.72095605
## [717,] 1.04268318 1.07231305
## [718,] 0.58359615 1.21954555
## [719,] 1.47098059 0.49855718
## [720,] 1.48084715 0.97248057
## [721,] 0.42059568 0.48714215
## [722,] 1.30976634 1.19626947
## [723,] 1.06218257 1.46461071
## [724,] 0.50323296 1.70440336
## [725,] 2.00346118 0.96413146
## [726,] 0.53304691 1.94526192
## [727,] 0.93573104 1.54852620
## [728,] 0.44424093 0.53788224
## [729,] 0.62376174 0.76646992
## [730,] 1.40859578 0.65276482
## [731,] 1.40249692 0.52285462
## [732,] 1.15721254 1.89114430
## [733,] 1.03608608 0.45284090
## [734,] 0.58621713 1.00552310
## [735,] 1.56992725 1.19843172
## [736,] 0.87014018 0.60363841
## [737,] 0.95938960 0.78043886
## [738,] 0.86674651 0.63415102
## [739,] 2.10010337 0.34946959
## [740,] 0.89058571 0.37816180
## [741,] 0.87345482 0.91400375
## [742,] 0.33455789 0.59056832
## [743,] 1.09608866 0.55840844
## [744,] 0.44544002 1.20725731
## [745,] 1.28826760 1.61228873
## [746,] 1.01345279 1.37232449
## [747,] 1.26350376 0.67658429
## [748,] 1.25190340 1.44747547
## [749,] 1.27863555 0.58983274
## [750,] 0.71892450 0.36714381
## [751,] 0.71684762 1.53571811
## [752,] 0.68603043 1.03450557
## [753,] 0.41205998 1.93692000
## [754,] 0.50608051 1.17464774
## [755,] 0.27533791 0.78894147
## [756,] 1.12427367 2.13090474
## [757,] 1.51139079 1.59280830
## [758,] 0.33947811 0.71355231
## [759,] 0.71507160 2.59816209
## [760,] 2.09234608 0.69258651
## [761,] 2.59373532 1.24923545
## [762,] 0.97872560 0.80804254
## [763,] 1.01046653 0.63431090
## [764,] 0.89466095 1.26004414
## [765,] 1.52505664 0.93192125
## [766,] 0.65600678 1.55014148
## [767,] 0.61492283 0.77335023
## [768,] 0.75713668 0.34716449
## [769,] 1.00082548 1.31670123
## [770,] 1.40659152 1.83096463
## [771,] 0.23604628 1.57065173
## [772,] 0.39847235 0.47639535
## [773,] 0.52017362 1.18383054
## [774,] 0.49184172 1.07053130
## [775,] 2.56787104 0.74042433
## [776,] 0.43728386 1.03076455
## [777,] 0.85319250 0.55859613
## [778,] 0.49222476 1.31369747
## [779,] 0.32164914 0.15110377
## [780,] 1.28600258 0.79744085
## [781,] 0.88979710 0.42592599
## [782,] 1.68947028 3.03491492
## [783,] 1.18989534 0.30871490
## [784,] 1.92428386 0.38726332
## [785,] 0.38437407 1.20220129
## [786,] 0.54221040 1.37099317
## [787,] 0.38168445 0.76633890
## [788,] 1.03915104 1.02235257
## [789,] 1.03084637 1.64684671
## [790,] 0.43061080 0.56642102
## [791,] 0.51328456 1.34688260
## [792,] 1.29644970 2.85866263
## [793,] 0.29692717 1.34853639
## [794,] 0.47025639 0.29698707
## [795,] 1.43226220 0.83750070
## [796,] 0.89525978 0.94574904
## [797,] 1.01548792 1.34242671
## [798,] 0.53487491 0.81501099
## [799,] 1.60291748 2.87071701
## [800,] 2.40589062 0.92662275
## [801,] 0.89229824 1.19750688
## [802,] 0.52245817 1.23072522
## [803,] 0.66333178 1.62519208
## [804,] 0.86317843 1.05664454
## [805,] 0.96383731 0.96521547
## [806,] 1.03285350 0.79248390
## [807,] 1.46118471 0.78940838
## [808,] 1.08545295 0.86022175
## [809,] 0.43867199 1.04348941
## [810,] 0.34237897 0.26747360
## [811,] 1.06087144 2.36608147
## [812,] 1.22402335 0.36713060
## [813,] 1.09436705 0.34783260
## [814,] 0.87654088 0.69008613
## [815,] 0.23048964 1.64716060
## [816,] 1.32844259 0.39071572
## [817,] 1.59733762 2.42567485
## [818,] 2.31894123 2.20890400
## [819,] 1.79336059 0.93774208
## [820,] 0.38595697 0.31228763
## [821,] 0.54853247 0.73380999
## [822,] 1.95429373 1.61757122
## [823,] 0.84709130 0.36778146
## [824,] 1.34862736 1.38422571
## [825,] 1.28158941 1.48996730
## [826,] 1.42333993 1.64183321
## [827,] 0.53614003 1.17500527
## [828,] 1.89775023 0.73837374
## [829,] 1.01919020 0.69129810
## [830,] 0.52752389 2.44558029
## [831,] 0.92894097 0.66764360
## [832,] 1.15387385 1.08354661
## [833,] 1.62051849 2.27909122
## [834,] 1.10286058 0.51916630
## [835,] 0.37372946 0.44865874
## [836,] 0.80461010 0.85691576
## [837,] 0.92769680 0.77493997
## [838,] 1.25660622 0.64623661
## [839,] 1.11020383 1.20550121
## [840,] 0.41985457 0.48252745
## [841,] 0.58316789 0.37142260
## [842,] 0.55927565 3.01739196
## [843,] 2.49294857 0.53028335
## [844,] 0.59207743 1.13499678
## [845,] 0.81168199 0.50509652
## [846,] 0.63828587 2.49604946
## [847,] 0.11687059 0.56405744
## [848,] 0.92071700 1.25707882
## [849,] 0.62811910 0.54452991
## [850,] 0.68864058 2.34108360
## [851,] 1.98813449 2.15758482
## [852,] 0.96636553 1.15987528
## [853,] 0.50012678 0.61436256
## [854,] 0.58883262 0.84292190
## [855,] 1.07669410 0.53429298
## [856,] 1.35131311 0.47019266
## [857,] 1.99604596 0.70804712
## [858,] 0.50319118 0.80845133
## [859,] 1.28516871 1.18583863
## [860,] 0.70068280 1.25085833
## [861,] 1.14200310 1.41738918
## [862,] 0.66431516 0.73082911
## [863,] 0.60222834 1.30557232
## [864,] 2.15892463 0.71163008
## [865,] 0.29001653 0.21353531
## [866,] 0.67092943 0.73784575
## [867,] 0.54472953 1.20616442
## [868,] 0.92688556 1.03085096
## [869,] 0.65136718 0.82225178
## [870,] 0.48609269 0.62278110
## [871,] 1.20615815 0.49707669
## [872,] 1.23351914 1.07788033
## [873,] 1.64521706 1.99862264
## [874,] 0.57741470 0.52914935
## [875,] 0.34012342 0.47300133
## [876,] 0.45124916 0.40009707
## [877,] 0.70131351 0.50857264
## [878,] 1.37888497 1.12916714
## [879,] 2.50002322 0.69370740
## [880,] 1.02864162 1.15426854
## [881,] 1.29019573 1.00376523
## [882,] 1.05102140 1.31999938
## [883,] 0.74388326 0.76755485
## [884,] 1.25291982 0.48428857
## [885,] 1.54070068 0.97412670
## [886,] 0.73817695 0.71396321
## [887,] 1.03070231 0.80910627
## [888,] 0.70733045 0.70076468
## [889,] 1.49814598 0.53227756
## [890,] 0.53024812 0.92260908
## [891,] 1.08486925 1.93509196
## [892,] 0.59940635 0.72311177
## [893,] 0.57450092 1.35341804
## [894,] 1.79405073 1.07477944
## [895,] 0.21499918 0.35022510
## [896,] 0.56998174 0.72052021
## [897,] 1.02402833 2.41580674
## [898,] 1.52631826 1.50248164
## [899,] 1.50824926 1.07372116
## [900,] 0.49334491 1.06871818
## [901,] 1.41522717 2.84522148
## [902,] 0.82790067 2.36896053
## [903,] 1.60375665 0.79416784
## [904,] 0.31340902 2.78047995
## [905,] 0.85047621 1.11246046
## [906,] 0.87359907 1.19679814
## [907,] 0.35962695 0.72873514
## [908,] 2.27598336 1.58114496
## [909,] 0.64843460 0.31206881
## [910,] 0.42488924 0.65103151
## [911,] 0.62593719 0.91524567
## [912,] 2.22092014 0.74220303
## [913,] 0.98008490 0.92449568
## [914,] 0.63629436 0.90078628
## [915,] 0.71336855 0.63313088
## [916,] 0.83124065 1.04338417
## [917,] 1.55360325 0.69699097
## [918,] 0.88758911 1.48967691
## [919,] 0.81449812 0.72860423
## [920,] 0.39791171 0.88909461
## [921,] 0.84335710 0.56526315
## [922,] 1.70928303 1.28024332
## [923,] 0.61505221 1.02085007
## [924,] 1.57561851 1.09573411
## [925,] 0.94258983 1.05489614
## [926,] 0.63001795 0.91220001
## [927,] 1.15983174 1.00649251
## [928,] 0.79192310 1.32475186
## [929,] 0.32612830 0.53902589
## [930,] 0.06507824 0.46683767
## [931,] 0.99942698 1.01099141
## [932,] 0.85819718 1.35158553
## [933,] 2.27027143 0.70256989
## [934,] 0.85748615 1.08375184
## [935,] 0.50984221 0.33737360
## [936,] 1.88506174 0.20621667
## [937,] 0.97008207 1.01653288
## [938,] 0.66035613 0.70767619
## [939,] 0.60292447 0.56466460
## [940,] 1.65659554 1.75001230
## [941,] 1.14036195 1.38914840
## [942,] 0.68841710 1.46491361
## [943,] 1.99922885 0.41672160
## [944,] 0.38753706 1.13124173
## [945,] 1.24490350 1.28126704
## [946,] 0.72778573 1.44571945
## [947,] 0.54906314 0.58453882
## [948,] 1.81598942 0.43549249
## [949,] 0.34033894 0.79579911
## [950,] 0.80848042 0.89848907
## [951,] 1.03917435 0.62845041
## [952,] 1.34181578 1.46777137
## [953,] 0.69368863 0.81119101
## [954,] 1.17242356 0.52461616
## [955,] 1.86842717 1.59466127
## [956,] 0.66835349 1.04369243
## [957,] 0.44460128 1.48373771
## [958,] 1.27759082 0.72572174
## [959,] 1.32060817 0.60752432
## [960,] 0.80202177 0.90071903
## [961,] 0.60817740 0.45518140
## [962,] 1.61741602 0.16625947
## [963,] 0.17790034 1.63388810
## [964,] 1.10735725 1.47725023
## [965,] 1.10967454 2.39772392
## [966,] 0.51993983 1.28254404
## [967,] 0.36946811 1.26305619
## [968,] 0.71352134 1.37349992
## [969,] 1.04524829 1.38124881
## [970,] 0.40747450 0.40406030
## [971,] 0.81115614 1.53944929
## [972,] 1.10944411 0.85249147
## [973,] 1.01315505 0.46267533
## [974,] 1.97265014 0.71529619
## [975,] 0.69378163 1.35512595
## [976,] 1.26242201 0.38993369
## [977,] 0.40984163 0.79500240
## [978,] 1.01136820 0.90020576
## [979,] 1.25290904 0.27863702
## [980,] 1.89168281 1.07017774
## [981,] 0.52213994 1.78279136
## [982,] 0.50858630 1.15337107
## [983,] 0.52640670 0.86512148
## [984,] 0.57360037 1.16488051
## [985,] 1.29013824 0.70920965
## [986,] 0.73614325 1.25243904
## [987,] 0.98448407 0.42224632
## [988,] 1.47705088 0.70825188
## [989,] 0.22408898 0.28103410
## [990,] 2.19876000 0.39834852
## [991,] 1.78030767 1.17992941
## [992,] 1.34823547 1.47540577
## [993,] 0.86095407 1.18363061
## [994,] 0.84773532 0.16795104
## [995,] 0.60582437 1.31650125
## [996,] 0.75056417 1.51325566
## [997,] 0.85279635 0.33303172
## [998,] 1.00470509 1.63167659
## [999,] 1.02546663 0.91057727
##
## $model.matrix
## (Intercept) microsite1 shrub_density
## 1 1 1 11
## 2 1 1 12
## 3 1 -1 0
## 4 1 -1 0
## 5 1 1 11
## 6 1 1 10
## 7 1 -1 0
## 8 1 -1 0
## 9 1 1 14
## 10 1 1 13
## 11 1 -1 0
## 12 1 -1 0
## 13 1 1 11
## 14 1 1 11
## 15 1 -1 0
## 16 1 1 10
## 17 1 1 11
## 18 1 1 11
## 19 1 1 10
## 20 1 -1 0
## 21 1 -1 0
## 22 1 -1 0
## 23 1 -1 0
##
## $terms
## pca_data_2023 ~ microsite * shrub_density
## attr(,"variables")
## list(pca_data_2023, microsite, shrub_density)
## attr(,"factors")
## microsite shrub_density microsite:shrub_density
## pca_data_2023 0 0 0
## microsite 1 0 1
## shrub_density 0 1 1
## attr(,"term.labels")
## [1] "microsite" "shrub_density"
## [3] "microsite:shrub_density"
## attr(,"order")
## [1] 1 1 2
## attr(,"intercept")
## [1] 1
## attr(,"response")
## [1] 1
## attr(,".Environment")
## <environment: R_GlobalEnv>
##
## attr(,"class")
## [1] "adonis"
dist_2023 <- vegdist(pca_data_2023, species = "bray")
res_2023 <- pcoa(dist_2023)
p2 <- as.data.frame(res_2023$vectors)%>%
dplyr::select(Axis.1, Axis.2) %>%
bind_cols(env_2023,.)
ggplot(p2, aes(Axis.1, Axis.2, group = microsite)) +
geom_point(aes(color = microsite)) +
geom_text(aes(label=plot), hjust = 0, vjust = 0, check_overlap = TRUE, nudge_x = 0.01)+
scale_color_brewer(palette = "Set1") +
labs(color = "", subtitle = "labels denote plot identity")
model02 <- betadisper(dist_2023, env_2023$microsite)
model02
##
## Homogeneity of multivariate dispersions
##
## Call: betadisper(d = dist_2023, group = env_2023$microsite)
##
## No. of Positive Eigenvalues: 15
## No. of Negative Eigenvalues: 7
##
## Average distance to median:
## Density Open
## 0.4638 0.5348
##
## Eigenvalues for PCoA axes:
## (Showing 8 of 22 eigenvalues)
## PCoA1 PCoA2 PCoA3 PCoA4 PCoA5 PCoA6 PCoA7 PCoA8
## 2.0914 1.4576 1.0790 0.5277 0.4666 0.3903 0.3612 0.1835
anova(model02)
## Analysis of Variance Table
##
## Response: Distances
## Df Sum Sq Mean Sq F value Pr(>F)
## Groups 1 0.02894 0.028937 1.5275 0.2301
## Residuals 21 0.39782 0.018944
permutest(model02,pairwise = TRUE, permutations = 99)
##
## Permutation test for homogeneity of multivariate dispersions
## Permutation: free
## Number of permutations: 99
##
## Response: Distances
## Df Sum Sq Mean Sq F N.Perm Pr(>F)
## Groups 1 0.02894 0.028937 1.5275 99 0.27
## Residuals 21 0.39782 0.018944
##
## Pairwise comparisons:
## (Observed p-value below diagonal, permuted p-value above diagonal)
## Density Open
## Density 0.25
## Open 0.23013
model02.HSD <- TukeyHSD(model02)
boxplot(model02)
model03 <- betadisper(dist_2023, env_2023$shrub_density)
model03
##
## Homogeneity of multivariate dispersions
##
## Call: betadisper(d = dist_2023, group = env_2023$shrub_density)
##
## No. of Positive Eigenvalues: 15
## No. of Negative Eigenvalues: 7
##
## Average distance to median:
## 0 10 11 12 13 14
## 0.5348 0.3051 0.4393 0.0000 0.0000 0.0000
##
## Eigenvalues for PCoA axes:
## (Showing 8 of 22 eigenvalues)
## PCoA1 PCoA2 PCoA3 PCoA4 PCoA5 PCoA6 PCoA7 PCoA8
## 2.0914 1.4576 1.0790 0.5277 0.4666 0.3903 0.3612 0.1835
anova(model03)
## Analysis of Variance Table
##
## Response: Distances
## Df Sum Sq Mean Sq F value Pr(>F)
## Groups 5 0.71375 0.142751 4.86 0.006083 **
## Residuals 17 0.49934 0.029373
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
permutest(model03,pairwise = TRUE, permutations = 99)
##
## Permutation test for homogeneity of multivariate dispersions
## Permutation: free
## Number of permutations: 99
##
## Response: Distances
## Df Sum Sq Mean Sq F N.Perm Pr(>F)
## Groups 5 0.71375 0.142751 4.86 99 0.02 *
## Residuals 17 0.49934 0.029373
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Pairwise comparisons:
## (Observed p-value below diagonal, permuted p-value above diagonal)
## 0 10 11 12 13 14
## 0 0.080000 0.230000
## 10 0.059818 0.460000
## 11 0.192393 0.435177
## 12
## 13
## 14
model03.HSD <- TukeyHSD(model03)
boxplot(model03)
model04 <- betadisper(dist_2023, env_2023$site)
model04
##
## Homogeneity of multivariate dispersions
##
## Call: betadisper(d = dist_2023, group = env_2023$site)
##
## No. of Positive Eigenvalues: 15
## No. of Negative Eigenvalues: 7
##
## Average distance to median:
## Carrizo Cuyama Tecopa
## 0.5093 0.4594 0.4488
##
## Eigenvalues for PCoA axes:
## (Showing 8 of 22 eigenvalues)
## PCoA1 PCoA2 PCoA3 PCoA4 PCoA5 PCoA6 PCoA7 PCoA8
## 2.0914 1.4576 1.0790 0.5277 0.4666 0.3903 0.3612 0.1835
anova(model04)
## Analysis of Variance Table
##
## Response: Distances
## Df Sum Sq Mean Sq F value Pr(>F)
## Groups 2 0.01656 0.0082795 0.4577 0.6392
## Residuals 20 0.36181 0.0180906
permutest(model04,pairwise = TRUE, permutations = 99)
##
## Permutation test for homogeneity of multivariate dispersions
## Permutation: free
## Number of permutations: 99
##
## Response: Distances
## Df Sum Sq Mean Sq F N.Perm Pr(>F)
## Groups 2 0.01656 0.0082795 0.4577 99 0.66
## Residuals 20 0.36181 0.0180906
##
## Pairwise comparisons:
## (Observed p-value below diagonal, permuted p-value above diagonal)
## Carrizo Cuyama Tecopa
## Carrizo 0.60000 0.14
## Cuyama 0.56406 0.90
## Tecopa 0.14036 0.89377
model04.HSD <- TukeyHSD(model04)
boxplot(model04)
### From running the community compositions both in 2022 and 2023 it
seems the community compositions across densities, microsites, and sites
are all somewhat similar with no significant differences.
photo_final <- read.csv("observations_final.csv")
summary(photo_final)
## region site site_code microsite
## Length:250716 Length:250716 Length:250716 Length:250716
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
## plot cam_ID month date
## Min. :1.000 Min. :1.000 Length:250716 Length:250716
## 1st Qu.:1.000 1st Qu.:1.000 Class :character Class :character
## Median :2.000 Median :1.000 Mode :character Mode :character
## Mean :1.772 Mean :1.498
## 3rd Qu.:2.000 3rd Qu.:2.000
## Max. :4.000 Max. :2.000
## year shrub_density rep identified_by
## Min. :2022 Min. : 0.000 Min. : 1 Length:250716
## 1st Qu.:2023 1st Qu.: 0.000 1st Qu.: 21744 Class :character
## Median :2023 Median :10.000 Median : 67344 Mode :character
## Mean :2023 Mean : 5.878 Mean : 77566
## 3rd Qu.:2023 3rd Qu.:11.000 3rd Qu.:130022
## Max. :2023 Max. :14.000 Max. :192701
## filename timestamp animal.hit class
## Length:250716 Length:250716 Min. :0.00000 Length:250716
## Class :character Class :character 1st Qu.:0.00000 Class :character
## Mode :character Mode :character Median :0.00000 Mode :character
## Mean :0.01923
## 3rd Qu.:0.00000
## Max. :1.00000
## order family genus species
## Length:250716 Length:250716 Length:250716 Length:250716
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
## common_name number_of_objects
## Length:250716 Min. : 1
## Class :character 1st Qu.: 1
## Mode :character Median : 1
## Mean : 1
## 3rd Qu.: 1
## Max. :12
photo_final <- photo_final %>%
filter(common_name != "Human")
photo_final <- photo_final %>%
filter(common_name != "Human-Camera Trapper")
photo_final <- photo_final %>%
filter(common_name != "Domestic Dog")
photo_final <- photo_final %>%
filter(common_name != "Vehicle")
photo_final <- photo_final %>%
dplyr::filter(common_name != "Insect")
photo_final <- photo_final %>%
dplyr::filter(common_name != "Animal")
photo_final <- photo_final %>%
dplyr::filter(common_name != "Bird")
count.hit_final <- photo_final %>%
count(animal.hit) %>%
na.omit()
summary(count.hit_final)
## animal.hit n
## Min. :0.00 Min. : 3717
## 1st Qu.:0.25 1st Qu.: 64261
## Median :0.50 Median :124806
## Mean :0.50 Mean :124806
## 3rd Qu.:0.75 3rd Qu.:185350
## Max. :1.00 Max. :245894
# Wow we have a 1.489% capture rate for the project!
### Animal Observations by Site_Code
animals_by_sitecode_final <- photo_final%>%
group_by(site_code, microsite, common_name) %>%
summarise(captures = sum(animal.hit), n = n())
## `summarise()` has grouped output by 'site_code', 'microsite'. You can override
## using the `.groups` argument.
animals_by_sitecode_final <- animals_by_sitecode_final %>%
filter(common_name != "Blank") %>% filter(common_name != "No CV Result")
animals_by_site_final <- photo_final %>% group_by(site,microsite,common_name) %>% summarise(captures = sum(animal.hit))
## `summarise()` has grouped output by 'site', 'microsite'. You can override using
## the `.groups` argument.
animals_by_site_final <- animals_by_site_final %>% filter(common_name != "Blank") %>% filter(common_name != "No CV Result")
animals_by_density_final<- photo_final %>% group_by(microsite,common_name) %>% summarise(captures = sum(animal.hit))
## `summarise()` has grouped output by 'microsite'. You can override using the
## `.groups` argument.
animals_by_density_final <- animals_by_density_final %>% filter(common_name != "Blank") %>% filter(common_name != "No CV Result")
Total_Observations_final <- photo_final %>% group_by(common_name) %>% summarise(total = sum(animal.hit)) %>% filter(common_name != "Blank") %>% filter(common_name != "No CV Result") %>% filter(common_name != "Mammal")
density_obvs_final <- merge(animals_by_density_final, Total_Observations_final, all = TRUE) %>% filter(common_name != "Mammal")
density_obvs_final$percent_presence <- density_obvs_final$captures/density_obvs_final$total
m3<- glm(total ~ microsite*common_name, family = "poisson", data = density_obvs_final)
anova(m3, test = "Chisq")
## Analysis of Deviance Table
##
## Model: poisson, link: log
##
## Response: total
##
## Terms added sequentially (first to last)
##
##
## Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL 55 27821
## microsite 1 29.6 54 27791 5.449e-08 ***
## common_name 32 27791.1 22 0 < 2.2e-16 ***
## microsite:common_name 22 0.0 0 0 1
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
e3 <- emmeans(m3, pairwise~common_name)
## NOTE: Results may be misleading due to involvement in interactions
e3
## $emmeans
## common_name emmean SE df asymp.LCL asymp.UCL
## American Robin nonEst NA NA NA NA
## Black-tailed Jackrabbit 5.878 0.03742 Inf 5.8044 5.951
## Black-throated Sparrow nonEst NA NA NA NA
## Blunt-nosed Leopard Lizard 1.386 0.35355 Inf 0.6933 2.079
## Bobcat 1.792 0.28868 Inf 1.2260 2.358
## Brewer's Blackbird 2.833 0.17150 Inf 2.4971 3.169
## California Ground Squirrel 5.447 0.04642 Inf 5.3557 5.538
## California Pocket Mouse 1.386 0.35355 Inf 0.6933 2.079
## California Quail 3.258 0.13867 Inf 2.9863 3.530
## California Thrasher nonEst NA NA NA NA
## Common Raven 4.394 0.07857 Inf 4.2405 4.548
## Coyote 4.898 0.06108 Inf 4.7781 5.018
## Desert Cottontail 4.143 0.08909 Inf 3.9685 4.318
## Desert Iguana nonEst NA NA NA NA
## Giant Kangaroo Rat 4.220 0.08575 Inf 4.0514 4.388
## Great White Egret nonEst NA NA NA NA
## Greater Roadrunner 1.946 0.26726 Inf 1.4221 2.470
## Heermann's Kangaroo Rat 7.782 0.01444 Inf 7.7537 7.810
## Horned Lark nonEst NA NA NA NA
## Killdeer nonEst NA NA NA NA
## Kit Fox 2.708 0.18257 Inf 2.3502 3.066
## Lark Sparrow 1.946 0.26726 Inf 1.4221 2.470
## Loggerhead Shrike 2.565 0.19612 Inf 2.1806 2.949
## Merriam's Kangaroo Rat 2.565 0.19612 Inf 2.1806 2.949
## Mohave Ground Squirrel nonEst NA NA NA NA
## Mourning Dove 1.609 0.31623 Inf 0.9896 2.229
## Nelson's Antelope Squirrel 5.142 0.05407 Inf 5.0357 5.248
## Red-tailed Hawk nonEst NA NA NA NA
## Salinas Pocket Mouse 1.386 0.35355 Inf 0.6933 2.079
## Say's Phoebe 1.386 0.35355 Inf 0.6933 2.079
## Vesper Sparrow 2.565 0.19612 Inf 2.1806 2.949
## Western whiptail nonEst NA NA NA NA
## White-tailed Antelope Squirrel 1.792 0.28868 Inf 1.2260 2.358
##
## Results are averaged over the levels of: microsite
## Results are given on the log (not the response) scale.
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate
## American Robin - (Black-tailed Jackrabbit) nonEst
## American Robin - (Black-throated Sparrow) nonEst
## American Robin - (Blunt-nosed Leopard Lizard) nonEst
## American Robin - Bobcat nonEst
## American Robin - Brewer's Blackbird nonEst
## American Robin - California Ground Squirrel nonEst
## American Robin - California Pocket Mouse nonEst
## American Robin - California Quail nonEst
## American Robin - California Thrasher nonEst
## American Robin - Common Raven nonEst
## American Robin - Coyote nonEst
## American Robin - Desert Cottontail nonEst
## American Robin - Desert Iguana nonEst
## American Robin - Giant Kangaroo Rat nonEst
## American Robin - Great White Egret nonEst
## American Robin - Greater Roadrunner nonEst
## American Robin - Heermann's Kangaroo Rat nonEst
## American Robin - Horned Lark nonEst
## American Robin - Killdeer nonEst
## American Robin - Kit Fox nonEst
## American Robin - Lark Sparrow nonEst
## American Robin - Loggerhead Shrike nonEst
## American Robin - Merriam's Kangaroo Rat nonEst
## American Robin - Mohave Ground Squirrel nonEst
## American Robin - Mourning Dove nonEst
## American Robin - Nelson's Antelope Squirrel nonEst
## American Robin - (Red-tailed Hawk) nonEst
## American Robin - Salinas Pocket Mouse nonEst
## American Robin - Say's Phoebe nonEst
## American Robin - Vesper Sparrow nonEst
## American Robin - Western whiptail nonEst
## American Robin - (White-tailed Antelope Squirrel) nonEst
## (Black-tailed Jackrabbit) - (Black-throated Sparrow) nonEst
## (Black-tailed Jackrabbit) - (Blunt-nosed Leopard Lizard) 4.4914
## (Black-tailed Jackrabbit) - Bobcat 4.0860
## (Black-tailed Jackrabbit) - Brewer's Blackbird 3.0445
## (Black-tailed Jackrabbit) - California Ground Squirrel 0.4310
## (Black-tailed Jackrabbit) - California Pocket Mouse 4.4914
## (Black-tailed Jackrabbit) - California Quail 2.6196
## (Black-tailed Jackrabbit) - California Thrasher nonEst
## (Black-tailed Jackrabbit) - Common Raven 1.4833
## (Black-tailed Jackrabbit) - Coyote 0.9799
## (Black-tailed Jackrabbit) - Desert Cottontail 1.7346
## (Black-tailed Jackrabbit) - Desert Iguana nonEst
## (Black-tailed Jackrabbit) - Giant Kangaroo Rat 1.6582
## (Black-tailed Jackrabbit) - Great White Egret nonEst
## (Black-tailed Jackrabbit) - Greater Roadrunner 3.9318
## (Black-tailed Jackrabbit) - Heermann's Kangaroo Rat -1.9042
## (Black-tailed Jackrabbit) - Horned Lark nonEst
## (Black-tailed Jackrabbit) - Killdeer nonEst
## (Black-tailed Jackrabbit) - Kit Fox 3.1697
## (Black-tailed Jackrabbit) - Lark Sparrow 3.9318
## (Black-tailed Jackrabbit) - Loggerhead Shrike 3.3128
## (Black-tailed Jackrabbit) - Merriam's Kangaroo Rat 3.3128
## (Black-tailed Jackrabbit) - Mohave Ground Squirrel nonEst
## (Black-tailed Jackrabbit) - Mourning Dove 4.2683
## (Black-tailed Jackrabbit) - Nelson's Antelope Squirrel 0.7361
## (Black-tailed Jackrabbit) - (Red-tailed Hawk) nonEst
## (Black-tailed Jackrabbit) - Salinas Pocket Mouse 4.4914
## (Black-tailed Jackrabbit) - Say's Phoebe 4.4914
## (Black-tailed Jackrabbit) - Vesper Sparrow 3.3128
## (Black-tailed Jackrabbit) - Western whiptail nonEst
## (Black-tailed Jackrabbit) - (White-tailed Antelope Squirrel) 4.0860
## (Black-throated Sparrow) - (Blunt-nosed Leopard Lizard) nonEst
## (Black-throated Sparrow) - Bobcat nonEst
## (Black-throated Sparrow) - Brewer's Blackbird nonEst
## (Black-throated Sparrow) - California Ground Squirrel nonEst
## (Black-throated Sparrow) - California Pocket Mouse nonEst
## (Black-throated Sparrow) - California Quail nonEst
## (Black-throated Sparrow) - California Thrasher nonEst
## (Black-throated Sparrow) - Common Raven nonEst
## (Black-throated Sparrow) - Coyote nonEst
## (Black-throated Sparrow) - Desert Cottontail nonEst
## (Black-throated Sparrow) - Desert Iguana nonEst
## (Black-throated Sparrow) - Giant Kangaroo Rat nonEst
## (Black-throated Sparrow) - Great White Egret nonEst
## (Black-throated Sparrow) - Greater Roadrunner nonEst
## (Black-throated Sparrow) - Heermann's Kangaroo Rat nonEst
## (Black-throated Sparrow) - Horned Lark nonEst
## (Black-throated Sparrow) - Killdeer nonEst
## (Black-throated Sparrow) - Kit Fox nonEst
## (Black-throated Sparrow) - Lark Sparrow nonEst
## (Black-throated Sparrow) - Loggerhead Shrike nonEst
## (Black-throated Sparrow) - Merriam's Kangaroo Rat nonEst
## (Black-throated Sparrow) - Mohave Ground Squirrel nonEst
## (Black-throated Sparrow) - Mourning Dove nonEst
## (Black-throated Sparrow) - Nelson's Antelope Squirrel nonEst
## (Black-throated Sparrow) - (Red-tailed Hawk) nonEst
## (Black-throated Sparrow) - Salinas Pocket Mouse nonEst
## (Black-throated Sparrow) - Say's Phoebe nonEst
## (Black-throated Sparrow) - Vesper Sparrow nonEst
## (Black-throated Sparrow) - Western whiptail nonEst
## (Black-throated Sparrow) - (White-tailed Antelope Squirrel) nonEst
## (Blunt-nosed Leopard Lizard) - Bobcat -0.4055
## (Blunt-nosed Leopard Lizard) - Brewer's Blackbird -1.4469
## (Blunt-nosed Leopard Lizard) - California Ground Squirrel -4.0604
## (Blunt-nosed Leopard Lizard) - California Pocket Mouse 0.0000
## (Blunt-nosed Leopard Lizard) - California Quail -1.8718
## (Blunt-nosed Leopard Lizard) - California Thrasher nonEst
## (Blunt-nosed Leopard Lizard) - Common Raven -3.0082
## (Blunt-nosed Leopard Lizard) - Coyote -3.5115
## (Blunt-nosed Leopard Lizard) - Desert Cottontail -2.7568
## (Blunt-nosed Leopard Lizard) - Desert Iguana nonEst
## (Blunt-nosed Leopard Lizard) - Giant Kangaroo Rat -2.8332
## (Blunt-nosed Leopard Lizard) - Great White Egret nonEst
## (Blunt-nosed Leopard Lizard) - Greater Roadrunner -0.5596
## (Blunt-nosed Leopard Lizard) - Heermann's Kangaroo Rat -6.3957
## (Blunt-nosed Leopard Lizard) - Horned Lark nonEst
## (Blunt-nosed Leopard Lizard) - Killdeer nonEst
## (Blunt-nosed Leopard Lizard) - Kit Fox -1.3218
## (Blunt-nosed Leopard Lizard) - Lark Sparrow -0.5596
## (Blunt-nosed Leopard Lizard) - Loggerhead Shrike -1.1787
## (Blunt-nosed Leopard Lizard) - Merriam's Kangaroo Rat -1.1787
## (Blunt-nosed Leopard Lizard) - Mohave Ground Squirrel nonEst
## (Blunt-nosed Leopard Lizard) - Mourning Dove -0.2231
## (Blunt-nosed Leopard Lizard) - Nelson's Antelope Squirrel -3.7554
## (Blunt-nosed Leopard Lizard) - (Red-tailed Hawk) nonEst
## (Blunt-nosed Leopard Lizard) - Salinas Pocket Mouse 0.0000
## (Blunt-nosed Leopard Lizard) - Say's Phoebe 0.0000
## (Blunt-nosed Leopard Lizard) - Vesper Sparrow -1.1787
## (Blunt-nosed Leopard Lizard) - Western whiptail nonEst
## (Blunt-nosed Leopard Lizard) - (White-tailed Antelope Squirrel) -0.4055
## Bobcat - Brewer's Blackbird -1.0415
## Bobcat - California Ground Squirrel -3.6550
## Bobcat - California Pocket Mouse 0.4055
## Bobcat - California Quail -1.4663
## Bobcat - California Thrasher nonEst
## Bobcat - Common Raven -2.6027
## Bobcat - Coyote -3.1061
## Bobcat - Desert Cottontail -2.3514
## Bobcat - Desert Iguana nonEst
## Bobcat - Giant Kangaroo Rat -2.4277
## Bobcat - Great White Egret nonEst
## Bobcat - Greater Roadrunner -0.1542
## Bobcat - Heermann's Kangaroo Rat -5.9902
## Bobcat - Horned Lark nonEst
## Bobcat - Killdeer nonEst
## Bobcat - Kit Fox -0.9163
## Bobcat - Lark Sparrow -0.1542
## Bobcat - Loggerhead Shrike -0.7732
## Bobcat - Merriam's Kangaroo Rat -0.7732
## Bobcat - Mohave Ground Squirrel nonEst
## Bobcat - Mourning Dove 0.1823
## Bobcat - Nelson's Antelope Squirrel -3.3499
## Bobcat - (Red-tailed Hawk) nonEst
## Bobcat - Salinas Pocket Mouse 0.4055
## Bobcat - Say's Phoebe 0.4055
## Bobcat - Vesper Sparrow -0.7732
## Bobcat - Western whiptail nonEst
## Bobcat - (White-tailed Antelope Squirrel) 0.0000
## Brewer's Blackbird - California Ground Squirrel -2.6135
## Brewer's Blackbird - California Pocket Mouse 1.4469
## Brewer's Blackbird - California Quail -0.4249
## Brewer's Blackbird - California Thrasher nonEst
## Brewer's Blackbird - Common Raven -1.5612
## Brewer's Blackbird - Coyote -2.0646
## Brewer's Blackbird - Desert Cottontail -1.3099
## Brewer's Blackbird - Desert Iguana nonEst
## Brewer's Blackbird - Giant Kangaroo Rat -1.3863
## Brewer's Blackbird - Great White Egret nonEst
## Brewer's Blackbird - Greater Roadrunner 0.8873
## Brewer's Blackbird - Heermann's Kangaroo Rat -4.9488
## Brewer's Blackbird - Horned Lark nonEst
## Brewer's Blackbird - Killdeer nonEst
## Brewer's Blackbird - Kit Fox 0.1252
## Brewer's Blackbird - Lark Sparrow 0.8873
## Brewer's Blackbird - Loggerhead Shrike 0.2683
## Brewer's Blackbird - Merriam's Kangaroo Rat 0.2683
## Brewer's Blackbird - Mohave Ground Squirrel nonEst
## Brewer's Blackbird - Mourning Dove 1.2238
## Brewer's Blackbird - Nelson's Antelope Squirrel -2.3085
## Brewer's Blackbird - (Red-tailed Hawk) nonEst
## Brewer's Blackbird - Salinas Pocket Mouse 1.4469
## Brewer's Blackbird - Say's Phoebe 1.4469
## Brewer's Blackbird - Vesper Sparrow 0.2683
## Brewer's Blackbird - Western whiptail nonEst
## Brewer's Blackbird - (White-tailed Antelope Squirrel) 1.0415
## California Ground Squirrel - California Pocket Mouse 4.0604
## California Ground Squirrel - California Quail 2.1886
## California Ground Squirrel - California Thrasher nonEst
## California Ground Squirrel - Common Raven 1.0523
## California Ground Squirrel - Coyote 0.5489
## California Ground Squirrel - Desert Cottontail 1.3036
## California Ground Squirrel - Desert Iguana nonEst
## California Ground Squirrel - Giant Kangaroo Rat 1.2272
## California Ground Squirrel - Great White Egret nonEst
## California Ground Squirrel - Greater Roadrunner 3.5008
## California Ground Squirrel - Heermann's Kangaroo Rat -2.3352
## California Ground Squirrel - Horned Lark nonEst
## California Ground Squirrel - Killdeer nonEst
## California Ground Squirrel - Kit Fox 2.7387
## California Ground Squirrel - Lark Sparrow 3.5008
## California Ground Squirrel - Loggerhead Shrike 2.8818
## California Ground Squirrel - Merriam's Kangaroo Rat 2.8818
## California Ground Squirrel - Mohave Ground Squirrel nonEst
## California Ground Squirrel - Mourning Dove 3.8373
## California Ground Squirrel - Nelson's Antelope Squirrel 0.3051
## California Ground Squirrel - (Red-tailed Hawk) nonEst
## California Ground Squirrel - Salinas Pocket Mouse 4.0604
## California Ground Squirrel - Say's Phoebe 4.0604
## California Ground Squirrel - Vesper Sparrow 2.8818
## California Ground Squirrel - Western whiptail nonEst
## California Ground Squirrel - (White-tailed Antelope Squirrel) 3.6550
## California Pocket Mouse - California Quail -1.8718
## California Pocket Mouse - California Thrasher nonEst
## California Pocket Mouse - Common Raven -3.0082
## California Pocket Mouse - Coyote -3.5115
## California Pocket Mouse - Desert Cottontail -2.7568
## California Pocket Mouse - Desert Iguana nonEst
## California Pocket Mouse - Giant Kangaroo Rat -2.8332
## California Pocket Mouse - Great White Egret nonEst
## California Pocket Mouse - Greater Roadrunner -0.5596
## California Pocket Mouse - Heermann's Kangaroo Rat -6.3957
## California Pocket Mouse - Horned Lark nonEst
## California Pocket Mouse - Killdeer nonEst
## California Pocket Mouse - Kit Fox -1.3218
## California Pocket Mouse - Lark Sparrow -0.5596
## California Pocket Mouse - Loggerhead Shrike -1.1787
## California Pocket Mouse - Merriam's Kangaroo Rat -1.1787
## California Pocket Mouse - Mohave Ground Squirrel nonEst
## California Pocket Mouse - Mourning Dove -0.2231
## California Pocket Mouse - Nelson's Antelope Squirrel -3.7554
## California Pocket Mouse - (Red-tailed Hawk) nonEst
## California Pocket Mouse - Salinas Pocket Mouse 0.0000
## California Pocket Mouse - Say's Phoebe 0.0000
## California Pocket Mouse - Vesper Sparrow -1.1787
## California Pocket Mouse - Western whiptail nonEst
## California Pocket Mouse - (White-tailed Antelope Squirrel) -0.4055
## California Quail - California Thrasher nonEst
## California Quail - Common Raven -1.1364
## California Quail - Coyote -1.6397
## California Quail - Desert Cottontail -0.8850
## California Quail - Desert Iguana nonEst
## California Quail - Giant Kangaroo Rat -0.9614
## California Quail - Great White Egret nonEst
## California Quail - Greater Roadrunner 1.3122
## California Quail - Heermann's Kangaroo Rat -4.5239
## California Quail - Horned Lark nonEst
## California Quail - Killdeer nonEst
## California Quail - Kit Fox 0.5500
## California Quail - Lark Sparrow 1.3122
## California Quail - Loggerhead Shrike 0.6931
## California Quail - Merriam's Kangaroo Rat 0.6931
## California Quail - Mohave Ground Squirrel nonEst
## California Quail - Mourning Dove 1.6487
## California Quail - Nelson's Antelope Squirrel -1.8836
## California Quail - (Red-tailed Hawk) nonEst
## California Quail - Salinas Pocket Mouse 1.8718
## California Quail - Say's Phoebe 1.8718
## California Quail - Vesper Sparrow 0.6931
## California Quail - Western whiptail nonEst
## California Quail - (White-tailed Antelope Squirrel) 1.4663
## California Thrasher - Common Raven nonEst
## California Thrasher - Coyote nonEst
## California Thrasher - Desert Cottontail nonEst
## California Thrasher - Desert Iguana nonEst
## California Thrasher - Giant Kangaroo Rat nonEst
## California Thrasher - Great White Egret nonEst
## California Thrasher - Greater Roadrunner nonEst
## California Thrasher - Heermann's Kangaroo Rat nonEst
## California Thrasher - Horned Lark nonEst
## California Thrasher - Killdeer nonEst
## California Thrasher - Kit Fox nonEst
## California Thrasher - Lark Sparrow nonEst
## California Thrasher - Loggerhead Shrike nonEst
## California Thrasher - Merriam's Kangaroo Rat nonEst
## California Thrasher - Mohave Ground Squirrel nonEst
## California Thrasher - Mourning Dove nonEst
## California Thrasher - Nelson's Antelope Squirrel nonEst
## California Thrasher - (Red-tailed Hawk) nonEst
## California Thrasher - Salinas Pocket Mouse nonEst
## California Thrasher - Say's Phoebe nonEst
## California Thrasher - Vesper Sparrow nonEst
## California Thrasher - Western whiptail nonEst
## California Thrasher - (White-tailed Antelope Squirrel) nonEst
## Common Raven - Coyote -0.5034
## Common Raven - Desert Cottontail 0.2513
## Common Raven - Desert Iguana nonEst
## Common Raven - Giant Kangaroo Rat 0.1749
## Common Raven - Great White Egret nonEst
## Common Raven - Greater Roadrunner 2.4485
## Common Raven - Heermann's Kangaroo Rat -3.3875
## Common Raven - Horned Lark nonEst
## Common Raven - Killdeer nonEst
## Common Raven - Kit Fox 1.6864
## Common Raven - Lark Sparrow 2.4485
## Common Raven - Loggerhead Shrike 1.8295
## Common Raven - Merriam's Kangaroo Rat 1.8295
## Common Raven - Mohave Ground Squirrel nonEst
## Common Raven - Mourning Dove 2.7850
## Common Raven - Nelson's Antelope Squirrel -0.7472
## Common Raven - (Red-tailed Hawk) nonEst
## Common Raven - Salinas Pocket Mouse 3.0082
## Common Raven - Say's Phoebe 3.0082
## Common Raven - Vesper Sparrow 1.8295
## Common Raven - Western whiptail nonEst
## Common Raven - (White-tailed Antelope Squirrel) 2.6027
## Coyote - Desert Cottontail 0.7547
## Coyote - Desert Iguana nonEst
## Coyote - Giant Kangaroo Rat 0.6783
## Coyote - Great White Egret nonEst
## Coyote - Greater Roadrunner 2.9519
## Coyote - Heermann's Kangaroo Rat -2.8841
## Coyote - Horned Lark nonEst
## Coyote - Killdeer nonEst
## Coyote - Kit Fox 2.1898
## Coyote - Lark Sparrow 2.9519
## Coyote - Loggerhead Shrike 2.3329
## Coyote - Merriam's Kangaroo Rat 2.3329
## Coyote - Mohave Ground Squirrel nonEst
## Coyote - Mourning Dove 3.2884
## Coyote - Nelson's Antelope Squirrel -0.2438
## Coyote - (Red-tailed Hawk) nonEst
## Coyote - Salinas Pocket Mouse 3.5115
## Coyote - Say's Phoebe 3.5115
## Coyote - Vesper Sparrow 2.3329
## Coyote - Western whiptail nonEst
## Coyote - (White-tailed Antelope Squirrel) 3.1061
## Desert Cottontail - Desert Iguana nonEst
## Desert Cottontail - Giant Kangaroo Rat -0.0764
## Desert Cottontail - Great White Egret nonEst
## Desert Cottontail - Greater Roadrunner 2.1972
## Desert Cottontail - Heermann's Kangaroo Rat -3.6388
## Desert Cottontail - Horned Lark nonEst
## Desert Cottontail - Killdeer nonEst
## Desert Cottontail - Kit Fox 1.4351
## Desert Cottontail - Lark Sparrow 2.1972
## Desert Cottontail - Loggerhead Shrike 1.5782
## Desert Cottontail - Merriam's Kangaroo Rat 1.5782
## Desert Cottontail - Mohave Ground Squirrel nonEst
## Desert Cottontail - Mourning Dove 2.5337
## Desert Cottontail - Nelson's Antelope Squirrel -0.9985
## Desert Cottontail - (Red-tailed Hawk) nonEst
## Desert Cottontail - Salinas Pocket Mouse 2.7568
## Desert Cottontail - Say's Phoebe 2.7568
## Desert Cottontail - Vesper Sparrow 1.5782
## Desert Cottontail - Western whiptail nonEst
## Desert Cottontail - (White-tailed Antelope Squirrel) 2.3514
## Desert Iguana - Giant Kangaroo Rat nonEst
## Desert Iguana - Great White Egret nonEst
## Desert Iguana - Greater Roadrunner nonEst
## Desert Iguana - Heermann's Kangaroo Rat nonEst
## Desert Iguana - Horned Lark nonEst
## Desert Iguana - Killdeer nonEst
## Desert Iguana - Kit Fox nonEst
## Desert Iguana - Lark Sparrow nonEst
## Desert Iguana - Loggerhead Shrike nonEst
## Desert Iguana - Merriam's Kangaroo Rat nonEst
## Desert Iguana - Mohave Ground Squirrel nonEst
## Desert Iguana - Mourning Dove nonEst
## Desert Iguana - Nelson's Antelope Squirrel nonEst
## Desert Iguana - (Red-tailed Hawk) nonEst
## Desert Iguana - Salinas Pocket Mouse nonEst
## Desert Iguana - Say's Phoebe nonEst
## Desert Iguana - Vesper Sparrow nonEst
## Desert Iguana - Western whiptail nonEst
## Desert Iguana - (White-tailed Antelope Squirrel) nonEst
## Giant Kangaroo Rat - Great White Egret nonEst
## Giant Kangaroo Rat - Greater Roadrunner 2.2736
## Giant Kangaroo Rat - Heermann's Kangaroo Rat -3.5625
## Giant Kangaroo Rat - Horned Lark nonEst
## Giant Kangaroo Rat - Killdeer nonEst
## Giant Kangaroo Rat - Kit Fox 1.5115
## Giant Kangaroo Rat - Lark Sparrow 2.2736
## Giant Kangaroo Rat - Loggerhead Shrike 1.6546
## Giant Kangaroo Rat - Merriam's Kangaroo Rat 1.6546
## Giant Kangaroo Rat - Mohave Ground Squirrel nonEst
## Giant Kangaroo Rat - Mourning Dove 2.6101
## Giant Kangaroo Rat - Nelson's Antelope Squirrel -0.9222
## Giant Kangaroo Rat - (Red-tailed Hawk) nonEst
## Giant Kangaroo Rat - Salinas Pocket Mouse 2.8332
## Giant Kangaroo Rat - Say's Phoebe 2.8332
## Giant Kangaroo Rat - Vesper Sparrow 1.6546
## Giant Kangaroo Rat - Western whiptail nonEst
## Giant Kangaroo Rat - (White-tailed Antelope Squirrel) 2.4277
## Great White Egret - Greater Roadrunner nonEst
## Great White Egret - Heermann's Kangaroo Rat nonEst
## Great White Egret - Horned Lark nonEst
## Great White Egret - Killdeer nonEst
## Great White Egret - Kit Fox nonEst
## Great White Egret - Lark Sparrow nonEst
## Great White Egret - Loggerhead Shrike nonEst
## Great White Egret - Merriam's Kangaroo Rat nonEst
## Great White Egret - Mohave Ground Squirrel nonEst
## Great White Egret - Mourning Dove nonEst
## Great White Egret - Nelson's Antelope Squirrel nonEst
## Great White Egret - (Red-tailed Hawk) nonEst
## Great White Egret - Salinas Pocket Mouse nonEst
## Great White Egret - Say's Phoebe nonEst
## Great White Egret - Vesper Sparrow nonEst
## Great White Egret - Western whiptail nonEst
## Great White Egret - (White-tailed Antelope Squirrel) nonEst
## Greater Roadrunner - Heermann's Kangaroo Rat -5.8361
## Greater Roadrunner - Horned Lark nonEst
## Greater Roadrunner - Killdeer nonEst
## Greater Roadrunner - Kit Fox -0.7621
## Greater Roadrunner - Lark Sparrow 0.0000
## Greater Roadrunner - Loggerhead Shrike -0.6190
## Greater Roadrunner - Merriam's Kangaroo Rat -0.6190
## Greater Roadrunner - Mohave Ground Squirrel nonEst
## Greater Roadrunner - Mourning Dove 0.3365
## Greater Roadrunner - Nelson's Antelope Squirrel -3.1958
## Greater Roadrunner - (Red-tailed Hawk) nonEst
## Greater Roadrunner - Salinas Pocket Mouse 0.5596
## Greater Roadrunner - Say's Phoebe 0.5596
## Greater Roadrunner - Vesper Sparrow -0.6190
## Greater Roadrunner - Western whiptail nonEst
## Greater Roadrunner - (White-tailed Antelope Squirrel) 0.1542
## Heermann's Kangaroo Rat - Horned Lark nonEst
## Heermann's Kangaroo Rat - Killdeer nonEst
## Heermann's Kangaroo Rat - Kit Fox 5.0739
## Heermann's Kangaroo Rat - Lark Sparrow 5.8361
## Heermann's Kangaroo Rat - Loggerhead Shrike 5.2170
## Heermann's Kangaroo Rat - Merriam's Kangaroo Rat 5.2170
## Heermann's Kangaroo Rat - Mohave Ground Squirrel nonEst
## Heermann's Kangaroo Rat - Mourning Dove 6.1725
## Heermann's Kangaroo Rat - Nelson's Antelope Squirrel 2.6403
## Heermann's Kangaroo Rat - (Red-tailed Hawk) nonEst
## Heermann's Kangaroo Rat - Salinas Pocket Mouse 6.3957
## Heermann's Kangaroo Rat - Say's Phoebe 6.3957
## Heermann's Kangaroo Rat - Vesper Sparrow 5.2170
## Heermann's Kangaroo Rat - Western whiptail nonEst
## Heermann's Kangaroo Rat - (White-tailed Antelope Squirrel) 5.9902
## Horned Lark - Killdeer nonEst
## Horned Lark - Kit Fox nonEst
## Horned Lark - Lark Sparrow nonEst
## Horned Lark - Loggerhead Shrike nonEst
## Horned Lark - Merriam's Kangaroo Rat nonEst
## Horned Lark - Mohave Ground Squirrel nonEst
## Horned Lark - Mourning Dove nonEst
## Horned Lark - Nelson's Antelope Squirrel nonEst
## Horned Lark - (Red-tailed Hawk) nonEst
## Horned Lark - Salinas Pocket Mouse nonEst
## Horned Lark - Say's Phoebe nonEst
## Horned Lark - Vesper Sparrow nonEst
## Horned Lark - Western whiptail nonEst
## Horned Lark - (White-tailed Antelope Squirrel) nonEst
## Killdeer - Kit Fox nonEst
## Killdeer - Lark Sparrow nonEst
## Killdeer - Loggerhead Shrike nonEst
## Killdeer - Merriam's Kangaroo Rat nonEst
## Killdeer - Mohave Ground Squirrel nonEst
## Killdeer - Mourning Dove nonEst
## Killdeer - Nelson's Antelope Squirrel nonEst
## Killdeer - (Red-tailed Hawk) nonEst
## Killdeer - Salinas Pocket Mouse nonEst
## Killdeer - Say's Phoebe nonEst
## Killdeer - Vesper Sparrow nonEst
## Killdeer - Western whiptail nonEst
## Killdeer - (White-tailed Antelope Squirrel) nonEst
## Kit Fox - Lark Sparrow 0.7621
## Kit Fox - Loggerhead Shrike 0.1431
## Kit Fox - Merriam's Kangaroo Rat 0.1431
## Kit Fox - Mohave Ground Squirrel nonEst
## Kit Fox - Mourning Dove 1.0986
## Kit Fox - Nelson's Antelope Squirrel -2.4336
## Kit Fox - (Red-tailed Hawk) nonEst
## Kit Fox - Salinas Pocket Mouse 1.3218
## Kit Fox - Say's Phoebe 1.3218
## Kit Fox - Vesper Sparrow 0.1431
## Kit Fox - Western whiptail nonEst
## Kit Fox - (White-tailed Antelope Squirrel) 0.9163
## Lark Sparrow - Loggerhead Shrike -0.6190
## Lark Sparrow - Merriam's Kangaroo Rat -0.6190
## Lark Sparrow - Mohave Ground Squirrel nonEst
## Lark Sparrow - Mourning Dove 0.3365
## Lark Sparrow - Nelson's Antelope Squirrel -3.1958
## Lark Sparrow - (Red-tailed Hawk) nonEst
## Lark Sparrow - Salinas Pocket Mouse 0.5596
## Lark Sparrow - Say's Phoebe 0.5596
## Lark Sparrow - Vesper Sparrow -0.6190
## Lark Sparrow - Western whiptail nonEst
## Lark Sparrow - (White-tailed Antelope Squirrel) 0.1542
## Loggerhead Shrike - Merriam's Kangaroo Rat 0.0000
## Loggerhead Shrike - Mohave Ground Squirrel nonEst
## Loggerhead Shrike - Mourning Dove 0.9555
## Loggerhead Shrike - Nelson's Antelope Squirrel -2.5767
## Loggerhead Shrike - (Red-tailed Hawk) nonEst
## Loggerhead Shrike - Salinas Pocket Mouse 1.1787
## Loggerhead Shrike - Say's Phoebe 1.1787
## Loggerhead Shrike - Vesper Sparrow 0.0000
## Loggerhead Shrike - Western whiptail nonEst
## Loggerhead Shrike - (White-tailed Antelope Squirrel) 0.7732
## Merriam's Kangaroo Rat - Mohave Ground Squirrel nonEst
## Merriam's Kangaroo Rat - Mourning Dove 0.9555
## Merriam's Kangaroo Rat - Nelson's Antelope Squirrel -2.5767
## Merriam's Kangaroo Rat - (Red-tailed Hawk) nonEst
## Merriam's Kangaroo Rat - Salinas Pocket Mouse 1.1787
## Merriam's Kangaroo Rat - Say's Phoebe 1.1787
## Merriam's Kangaroo Rat - Vesper Sparrow 0.0000
## Merriam's Kangaroo Rat - Western whiptail nonEst
## Merriam's Kangaroo Rat - (White-tailed Antelope Squirrel) 0.7732
## Mohave Ground Squirrel - Mourning Dove nonEst
## Mohave Ground Squirrel - Nelson's Antelope Squirrel nonEst
## Mohave Ground Squirrel - (Red-tailed Hawk) nonEst
## Mohave Ground Squirrel - Salinas Pocket Mouse nonEst
## Mohave Ground Squirrel - Say's Phoebe nonEst
## Mohave Ground Squirrel - Vesper Sparrow nonEst
## Mohave Ground Squirrel - Western whiptail nonEst
## Mohave Ground Squirrel - (White-tailed Antelope Squirrel) nonEst
## Mourning Dove - Nelson's Antelope Squirrel -3.5322
## Mourning Dove - (Red-tailed Hawk) nonEst
## Mourning Dove - Salinas Pocket Mouse 0.2231
## Mourning Dove - Say's Phoebe 0.2231
## Mourning Dove - Vesper Sparrow -0.9555
## Mourning Dove - Western whiptail nonEst
## Mourning Dove - (White-tailed Antelope Squirrel) -0.1823
## Nelson's Antelope Squirrel - (Red-tailed Hawk) nonEst
## Nelson's Antelope Squirrel - Salinas Pocket Mouse 3.7554
## Nelson's Antelope Squirrel - Say's Phoebe 3.7554
## Nelson's Antelope Squirrel - Vesper Sparrow 2.5767
## Nelson's Antelope Squirrel - Western whiptail nonEst
## Nelson's Antelope Squirrel - (White-tailed Antelope Squirrel) 3.3499
## (Red-tailed Hawk) - Salinas Pocket Mouse nonEst
## (Red-tailed Hawk) - Say's Phoebe nonEst
## (Red-tailed Hawk) - Vesper Sparrow nonEst
## (Red-tailed Hawk) - Western whiptail nonEst
## (Red-tailed Hawk) - (White-tailed Antelope Squirrel) nonEst
## Salinas Pocket Mouse - Say's Phoebe 0.0000
## Salinas Pocket Mouse - Vesper Sparrow -1.1787
## Salinas Pocket Mouse - Western whiptail nonEst
## Salinas Pocket Mouse - (White-tailed Antelope Squirrel) -0.4055
## Say's Phoebe - Vesper Sparrow -1.1787
## Say's Phoebe - Western whiptail nonEst
## Say's Phoebe - (White-tailed Antelope Squirrel) -0.4055
## Vesper Sparrow - Western whiptail nonEst
## Vesper Sparrow - (White-tailed Antelope Squirrel) 0.7732
## Western whiptail - (White-tailed Antelope Squirrel) nonEst
## SE df z.ratio p.value
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## 0.3555 Inf 12.633 <.0001
## 0.2911 Inf 14.037 <.0001
## 0.1755 Inf 17.344 <.0001
## 0.0596 Inf 7.228 <.0001
## 0.3555 Inf 12.633 <.0001
## 0.1436 Inf 18.238 <.0001
## NA NA NA NA
## 0.0870 Inf 17.044 <.0001
## 0.0716 Inf 13.679 <.0001
## 0.0966 Inf 17.951 <.0001
## NA NA NA NA
## 0.0936 Inf 17.724 <.0001
## NA NA NA NA
## 0.2699 Inf 14.569 <.0001
## 0.0401 Inf -47.470 <.0001
## NA NA NA NA
## NA NA NA NA
## 0.1864 Inf 17.007 <.0001
## 0.2699 Inf 14.569 <.0001
## 0.1997 Inf 16.593 <.0001
## 0.1997 Inf 16.593 <.0001
## NA NA NA NA
## 0.3184 Inf 13.404 <.0001
## 0.0658 Inf 11.193 <.0001
## NA NA NA NA
## 0.3555 Inf 12.633 <.0001
## 0.3555 Inf 12.633 <.0001
## 0.1997 Inf 16.593 <.0001
## NA NA NA NA
## 0.2911 Inf 14.037 <.0001
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## 0.4564 Inf -0.888 1.0000
## 0.3930 Inf -3.682 0.0729
## 0.3566 Inf -11.387 <.0001
## 0.5000 Inf 0.000 1.0000
## 0.3798 Inf -4.929 0.0004
## NA NA NA NA
## 0.3622 Inf -8.306 <.0001
## 0.3588 Inf -9.787 <.0001
## 0.3646 Inf -7.561 <.0001
## NA NA NA NA
## 0.3638 Inf -7.788 <.0001
## NA NA NA NA
## 0.4432 Inf -1.263 1.0000
## 0.3538 Inf -18.075 <.0001
## NA NA NA NA
## NA NA NA NA
## 0.3979 Inf -3.322 0.2085
## 0.4432 Inf -1.263 1.0000
## 0.4043 Inf -2.915 0.4964
## 0.4043 Inf -2.915 0.4964
## NA NA NA NA
## 0.4743 Inf -0.470 1.0000
## 0.3577 Inf -10.500 <.0001
## NA NA NA NA
## 0.5000 Inf 0.000 1.0000
## 0.5000 Inf 0.000 1.0000
## 0.4043 Inf -2.915 0.4964
## NA NA NA NA
## 0.4564 Inf -0.888 1.0000
## 0.3358 Inf -3.102 0.3488
## 0.2924 Inf -12.501 <.0001
## 0.4564 Inf 0.888 1.0000
## 0.3203 Inf -4.579 0.0021
## NA NA NA NA
## 0.2992 Inf -8.700 <.0001
## 0.2951 Inf -10.527 <.0001
## 0.3021 Inf -7.783 <.0001
## NA NA NA NA
## 0.3011 Inf -8.062 <.0001
## NA NA NA NA
## 0.3934 Inf -0.392 1.0000
## 0.2890 Inf -20.725 <.0001
## NA NA NA NA
## NA NA NA NA
## 0.3416 Inf -2.683 0.6900
## 0.3934 Inf -0.392 1.0000
## 0.3490 Inf -2.216 0.9487
## 0.3490 Inf -2.216 0.9487
## NA NA NA NA
## 0.4282 Inf 0.426 1.0000
## 0.2937 Inf -11.406 <.0001
## NA NA NA NA
## 0.4564 Inf 0.888 1.0000
## 0.4564 Inf 0.888 1.0000
## 0.3490 Inf -2.216 0.9487
## NA NA NA NA
## 0.4082 Inf 0.000 1.0000
## 0.1777 Inf -14.710 <.0001
## 0.3930 Inf 3.682 0.0729
## 0.2206 Inf -1.926 0.9926
## NA NA NA NA
## 0.1886 Inf -8.276 <.0001
## 0.1821 Inf -11.341 <.0001
## 0.1933 Inf -6.778 <.0001
## NA NA NA NA
## 0.1917 Inf -7.230 <.0001
## NA NA NA NA
## 0.3176 Inf 2.794 0.5984
## 0.1721 Inf -28.754 <.0001
## NA NA NA NA
## NA NA NA NA
## 0.2505 Inf 0.500 1.0000
## 0.3176 Inf 2.794 0.5984
## 0.2605 Inf 1.030 1.0000
## 0.2605 Inf 1.030 1.0000
## NA NA NA NA
## 0.3597 Inf 3.402 0.1687
## 0.1798 Inf -12.837 <.0001
## NA NA NA NA
## 0.3930 Inf 3.682 0.0729
## 0.3930 Inf 3.682 0.0729
## 0.2605 Inf 1.030 1.0000
## NA NA NA NA
## 0.3358 Inf 3.102 0.3488
## 0.3566 Inf 11.387 <.0001
## 0.1462 Inf 14.966 <.0001
## NA NA NA NA
## 0.0913 Inf 11.531 <.0001
## 0.0767 Inf 7.154 <.0001
## 0.1005 Inf 12.977 <.0001
## NA NA NA NA
## 0.0975 Inf 12.586 <.0001
## NA NA NA NA
## 0.2713 Inf 12.906 <.0001
## 0.0486 Inf -48.032 <.0001
## NA NA NA NA
## NA NA NA NA
## 0.1884 Inf 14.538 <.0001
## 0.2713 Inf 12.906 <.0001
## 0.2015 Inf 14.299 <.0001
## 0.2015 Inf 14.299 <.0001
## NA NA NA NA
## 0.3196 Inf 12.006 <.0001
## 0.0713 Inf 4.281 0.0079
## NA NA NA NA
## 0.3566 Inf 11.387 <.0001
## 0.3566 Inf 11.387 <.0001
## 0.2015 Inf 14.299 <.0001
## NA NA NA NA
## 0.2924 Inf 12.501 <.0001
## 0.3798 Inf -4.929 0.0004
## NA NA NA NA
## 0.3622 Inf -8.306 <.0001
## 0.3588 Inf -9.787 <.0001
## 0.3646 Inf -7.561 <.0001
## NA NA NA NA
## 0.3638 Inf -7.788 <.0001
## NA NA NA NA
## 0.4432 Inf -1.263 1.0000
## 0.3538 Inf -18.075 <.0001
## NA NA NA NA
## NA NA NA NA
## 0.3979 Inf -3.322 0.2085
## 0.4432 Inf -1.263 1.0000
## 0.4043 Inf -2.915 0.4964
## 0.4043 Inf -2.915 0.4964
## NA NA NA NA
## 0.4743 Inf -0.470 1.0000
## 0.3577 Inf -10.500 <.0001
## NA NA NA NA
## 0.5000 Inf 0.000 1.0000
## 0.5000 Inf 0.000 1.0000
## 0.4043 Inf -2.915 0.4964
## NA NA NA NA
## 0.4564 Inf -0.888 1.0000
## NA NA NA NA
## 0.1594 Inf -7.130 <.0001
## 0.1515 Inf -10.821 <.0001
## 0.1648 Inf -5.370 <.0001
## NA NA NA NA
## 0.1630 Inf -5.897 <.0001
## NA NA NA NA
## 0.3011 Inf 4.358 0.0057
## 0.1394 Inf -32.447 <.0001
## NA NA NA NA
## NA NA NA NA
## 0.2293 Inf 2.399 0.8778
## 0.3011 Inf 4.358 0.0057
## 0.2402 Inf 2.886 0.5211
## 0.2402 Inf 2.886 0.5211
## NA NA NA NA
## 0.3453 Inf 4.775 0.0009
## 0.1488 Inf -12.655 <.0001
## NA NA NA NA
## 0.3798 Inf 4.929 0.0004
## 0.3798 Inf 4.929 0.0004
## 0.2402 Inf 2.886 0.5211
## NA NA NA NA
## 0.3203 Inf 4.579 0.0021
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## 0.0995 Inf -5.058 0.0002
## 0.1188 Inf 2.116 0.9713
## NA NA NA NA
## 0.1163 Inf 1.504 0.9999
## NA NA NA NA
## 0.2786 Inf 8.790 <.0001
## 0.0799 Inf -42.406 <.0001
## NA NA NA NA
## NA NA NA NA
## 0.1988 Inf 8.485 <.0001
## 0.2786 Inf 8.790 <.0001
## 0.2113 Inf 8.660 <.0001
## 0.2113 Inf 8.660 <.0001
## NA NA NA NA
## 0.3258 Inf 8.547 <.0001
## 0.0954 Inf -7.834 <.0001
## NA NA NA NA
## 0.3622 Inf 8.306 <.0001
## 0.3622 Inf 8.306 <.0001
## 0.2113 Inf 8.660 <.0001
## NA NA NA NA
## 0.2992 Inf 8.700 <.0001
## 0.1080 Inf 6.987 <.0001
## NA NA NA NA
## 0.1053 Inf 6.443 <.0001
## NA NA NA NA
## 0.2742 Inf 10.767 <.0001
## 0.0628 Inf -45.948 <.0001
## NA NA NA NA
## NA NA NA NA
## 0.1925 Inf 11.374 <.0001
## 0.2742 Inf 10.767 <.0001
## 0.2054 Inf 11.357 <.0001
## 0.2054 Inf 11.357 <.0001
## NA NA NA NA
## 0.3221 Inf 10.210 <.0001
## 0.0816 Inf -2.989 0.4360
## NA NA NA NA
## 0.3588 Inf 9.787 <.0001
## 0.3588 Inf 9.787 <.0001
## 0.2054 Inf 11.357 <.0001
## NA NA NA NA
## 0.2951 Inf 10.527 <.0001
## NA NA NA NA
## 0.1237 Inf -0.618 1.0000
## NA NA NA NA
## 0.2817 Inf 7.799 <.0001
## 0.0903 Inf -40.319 <.0001
## NA NA NA NA
## NA NA NA NA
## 0.2031 Inf 7.064 <.0001
## 0.2817 Inf 7.799 <.0001
## 0.2154 Inf 7.327 <.0001
## 0.2154 Inf 7.327 <.0001
## NA NA NA NA
## 0.3285 Inf 7.712 <.0001
## 0.1042 Inf -9.582 <.0001
## NA NA NA NA
## 0.3646 Inf 7.561 <.0001
## 0.3646 Inf 7.561 <.0001
## 0.2154 Inf 7.327 <.0001
## NA NA NA NA
## 0.3021 Inf 7.783 <.0001
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## 0.2807 Inf 8.100 <.0001
## 0.0870 Inf -40.968 <.0001
## NA NA NA NA
## NA NA NA NA
## 0.2017 Inf 7.493 <.0001
## 0.2807 Inf 8.100 <.0001
## 0.2140 Inf 7.730 <.0001
## 0.2140 Inf 7.730 <.0001
## NA NA NA NA
## 0.3276 Inf 7.966 <.0001
## 0.1014 Inf -9.096 <.0001
## NA NA NA NA
## 0.3638 Inf 7.788 <.0001
## 0.3638 Inf 7.788 <.0001
## 0.2140 Inf 7.730 <.0001
## NA NA NA NA
## 0.3011 Inf 8.062 <.0001
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## 0.2677 Inf -21.805 <.0001
## NA NA NA NA
## NA NA NA NA
## 0.3237 Inf -2.355 0.8988
## 0.3780 Inf 0.000 1.0000
## 0.3315 Inf -1.867 0.9955
## 0.3315 Inf -1.867 0.9955
## NA NA NA NA
## 0.4140 Inf 0.813 1.0000
## 0.2727 Inf -11.720 <.0001
## NA NA NA NA
## 0.4432 Inf 1.263 1.0000
## 0.4432 Inf 1.263 1.0000
## 0.3315 Inf -1.867 0.9955
## NA NA NA NA
## 0.3934 Inf 0.392 1.0000
## NA NA NA NA
## NA NA NA NA
## 0.1831 Inf 27.704 <.0001
## 0.2677 Inf 21.805 <.0001
## 0.1966 Inf 26.530 <.0001
## 0.1966 Inf 26.530 <.0001
## NA NA NA NA
## 0.3166 Inf 19.499 <.0001
## 0.0560 Inf 47.174 <.0001
## NA NA NA NA
## 0.3538 Inf 18.075 <.0001
## 0.3538 Inf 18.075 <.0001
## 0.1966 Inf 26.530 <.0001
## NA NA NA NA
## 0.2890 Inf 20.725 <.0001
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## 0.3237 Inf 2.355 0.8988
## 0.2679 Inf 0.534 1.0000
## 0.2679 Inf 0.534 1.0000
## NA NA NA NA
## 0.3651 Inf 3.009 0.4201
## 0.1904 Inf -12.781 <.0001
## NA NA NA NA
## 0.3979 Inf 3.322 0.2085
## 0.3979 Inf 3.322 0.2085
## 0.2679 Inf 0.534 1.0000
## NA NA NA NA
## 0.3416 Inf 2.683 0.6900
## 0.3315 Inf -1.867 0.9955
## 0.3315 Inf -1.867 0.9955
## NA NA NA NA
## 0.4140 Inf 0.813 1.0000
## 0.2727 Inf -11.720 <.0001
## NA NA NA NA
## 0.4432 Inf 1.263 1.0000
## 0.4432 Inf 1.263 1.0000
## 0.3315 Inf -1.867 0.9955
## NA NA NA NA
## 0.3934 Inf 0.392 1.0000
## 0.2774 Inf 0.000 1.0000
## NA NA NA NA
## 0.3721 Inf 2.568 0.7761
## 0.2034 Inf -12.666 <.0001
## NA NA NA NA
## 0.4043 Inf 2.915 0.4964
## 0.4043 Inf 2.915 0.4964
## 0.2774 Inf 0.000 1.0000
## NA NA NA NA
## 0.3490 Inf 2.216 0.9487
## NA NA NA NA
## 0.3721 Inf 2.568 0.7761
## 0.2034 Inf -12.666 <.0001
## NA NA NA NA
## 0.4043 Inf 2.915 0.4964
## 0.4043 Inf 2.915 0.4964
## 0.2774 Inf 0.000 1.0000
## NA NA NA NA
## 0.3490 Inf 2.216 0.9487
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## 0.3208 Inf -11.010 <.0001
## NA NA NA NA
## 0.4743 Inf 0.470 1.0000
## 0.4743 Inf 0.470 1.0000
## 0.3721 Inf -2.568 0.7761
## NA NA NA NA
## 0.4282 Inf -0.426 1.0000
## NA NA NA NA
## 0.3577 Inf 10.500 <.0001
## 0.3577 Inf 10.500 <.0001
## 0.2034 Inf 12.666 <.0001
## NA NA NA NA
## 0.2937 Inf 11.406 <.0001
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## NA NA NA NA
## 0.5000 Inf 0.000 1.0000
## 0.4043 Inf -2.915 0.4964
## NA NA NA NA
## 0.4564 Inf -0.888 1.0000
## 0.4043 Inf -2.915 0.4964
## NA NA NA NA
## 0.4564 Inf -0.888 1.0000
## NA NA NA NA
## 0.3490 Inf 2.216 0.9487
## NA NA NA NA
##
## Results are averaged over the levels of: microsite
## Results are given on the log (not the response) scale.
## P value adjustment: tukey method for comparing a family of 33 estimates
animals_density_final <- photo_final %>% group_by(site_code,microsite,plot, shrub_density, common_name) %>% summarise(captures = sum(animal.hit))
## `summarise()` has grouped output by 'site_code', 'microsite', 'plot',
## 'shrub_density'. You can override using the `.groups` argument.
animals_density_final <- animals_density_final %>% filter(common_name != "Blank") %>% filter(common_name != "Mammal")
pca_data_final <- animals_density_final ### Created new df for pca data
pca_data_final <- pca_data_final %>%
spread(common_name, captures) %>%
ungroup() %>%
dplyr::select(-site_code, -microsite, -plot) %>%
replace(is.na(.),0)
dim(pca_data_final)
## [1] 24 35
env_final <- read.csv("environment_final.csv") ### Drop Tecopa open 1, Tecopa open 4, since they have no animal observations.
dim(env_final)
## [1] 24 5
model010 <- adonis(pca_data_final ~ microsite*shrub_density, data = env_final)
## 'adonis' will be deprecated: use 'adonis2' instead
model010
## $aov.tab
## Permutation: free
## Number of permutations: 999
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## microsite 1 0.2680 0.26799 1.0165 0.04244 0.376
## shrub_density 1 0.5102 0.51024 1.9354 0.08081 0.071 .
## Residuals 21 5.5363 0.26363 0.87675
## Total 23 6.3145 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $call
## adonis(formula = pca_data_final ~ microsite * shrub_density,
## data = env_final)
##
## $coefficients
## shrub_density American Robin Black-tailed Jackrabbit
## (Intercept) -10.557692 4.166667e-02 -11.346154
## microsite1 -14.057692 -4.166667e-02 -22.012821
## shrub_density 2.876923 9.476236e-19 4.661538
## microsite1:shrub_density NA NA NA
## Black-throated Sparrow Blunt-nosed Leopard Lizard
## (Intercept) 4.166667e-02 1.1185897
## microsite1 -4.166667e-02 1.0352564
## shrub_density 4.564605e-18 -0.1692308
## microsite1:shrub_density NA NA
## Bobcat Brewer's Blackbird
## (Intercept) -0.7019231 -2.4070513
## microsite1 -1.1185897 -2.8237179
## shrub_density 0.1692308 0.5538462
## microsite1:shrub_density NA NA
## California Ground Squirrel California Pocket Mouse
## (Intercept) 19.618590 0.33974359
## microsite1 13.868590 0.17307692
## shrub_density -1.769231 -0.03076923
## microsite1:shrub_density NA NA
## California Quail California Thrasher Common Raven
## (Intercept) -6.185897 -1.0 2.5961538
## microsite1 -7.352564 -1.0 -1.9038462
## shrub_density 1.292308 0.2 0.1384615
## microsite1:shrub_density NA NA NA
## Coyote Desert Cottontail Desert Iguana
## (Intercept) 3.2467949 -27.230769 1.958333e+00
## microsite1 -2.8365385 -28.897436 -1.958333e+00
## shrub_density 0.4153846 5.307692 3.783904e-16
## microsite1:shrub_density NA NA NA
## Giant Kangaroo Rat Great White Egret
## (Intercept) 6.8141026 4.166667e-02
## microsite1 4.6474359 -4.166667e-02
## shrub_density -0.7076923 4.564605e-18
## microsite1:shrub_density NA NA
## Greater Roadrunner Heermann's Kangaroo Rat Horned Lark
## (Intercept) -4.3814103 125.403846 0.38461538
## microsite1 -4.4647436 50.903846 0.38461538
## shrub_density 0.8307692 -4.538462 -0.04615385
## microsite1:shrub_density NA NA NA
## Killdeer Kit Fox Lark Sparrow
## (Intercept) 4.166667e-02 2.5288462 -1.8717949
## microsite1 -4.166667e-02 2.1121795 -2.2051282
## shrub_density 4.564605e-18 -0.3384615 0.3846154
## microsite1:shrub_density NA NA NA
## Loggerhead Shrike Merriam's Kangaroo Rat
## (Intercept) -1.1025641 1.06089744
## microsite1 -1.4358974 0.14423077
## shrub_density 0.2923077 -0.09230769
## microsite1:shrub_density NA NA
## Mohave Ground Squirrel Mourning Dove
## (Intercept) 2.916667e-01 -1.9551282
## microsite1 -2.916667e-01 -2.1217949
## shrub_density -1.570024e-16 0.3846154
## microsite1:shrub_density NA NA
## Nelson's Antelope Squirrel No CV Result
## (Intercept) 18.894231 -0.8685897
## microsite1 13.977564 -0.9519231
## shrub_density -2.092308 0.1692308
## microsite1:shrub_density NA NA
## Red-tailed Hawk Salinas Pocket Mouse Say's Phoebe
## (Intercept) -0.5641026 -0.35256410 0.253205128
## microsite1 -0.5641026 -0.51923077 0.003205128
## shrub_density 0.1076923 0.09230769 -0.015384615
## microsite1:shrub_density NA NA NA
## Vesper Sparrow Western whiptail
## (Intercept) 1.5801282 0.12820513
## microsite1 1.4967949 0.12820513
## shrub_density -0.1846154 -0.01538462
## microsite1:shrub_density NA NA
## White-tailed Antelope Squirrel
## (Intercept) 0.33653846
## microsite1 -0.08012821
## shrub_density -0.01538462
## microsite1:shrub_density NA
##
## $coef.sites
## 1 2 3 4
## (Intercept) 0.78080317 0.82434750 0.98306093 0.76605527
## microsite1 0.17504154 0.26134577 0.28849450 0.24337903
## shrub_density -0.03080745 -0.04609079 -0.04138812 -0.03373393
## microsite1:shrub_density NA NA NA NA
## 5 6 7 8
## (Intercept) 0.84838652 0.83124969 0.86997845 0.76272593
## microsite1 0.30313950 0.28542834 0.29904628 0.20499025
## shrub_density -0.05068255 -0.04597727 -0.04282439 -0.02976002
## microsite1:shrub_density NA NA NA NA
## 9 10 11 12
## (Intercept) 1.7481015 1.5203179 1.00167997 1.0125329
## microsite1 1.0946409 0.9430041 0.41975528 0.4948288
## shrub_density -0.1923781 -0.1650103 -0.07142752 -0.0794049
## microsite1:shrub_density NA NA NA NA
## 13 14 15 16
## (Intercept) 0.88500919 0.84806327 0.8076157 0.83755826
## microsite1 0.18017241 0.07715532 0.2071152 0.27863228
## shrub_density -0.03799694 -0.02089895 -0.0336203 -0.04242666
## microsite1:shrub_density NA NA NA NA
## 17 18 19 20
## (Intercept) 0.04881892 0.31671402 0.25149293 0.07189331
## microsite1 -0.82473427 -0.56994858 -0.63539881 -0.82608604
## shrub_density 0.13158888 0.08613734 0.09735222 0.13310018
## microsite1:shrub_density NA NA NA NA
## 21 22 23 24
## (Intercept) 1.12355908 1.11712532 1.06379973 0.830255162
## microsite1 0.41343026 0.46257310 0.31720419 0.079719825
## shrub_density -0.06585209 -0.07418334 -0.04509589 -0.006831989
## microsite1:shrub_density NA NA NA NA
##
## $f.perms
## [,1] [,2]
## [1,] 0.42567613 1.6145440
## [2,] 0.55164061 1.0902870
## [3,] 2.11448261 1.7774341
## [4,] 1.36365233 1.2634207
## [5,] 0.77722767 0.3427893
## [6,] 0.82030023 0.9880416
## [7,] 0.38188850 0.5671524
## [8,] 0.33887034 0.9687802
## [9,] 0.41865359 0.1675109
## [10,] 1.21971451 1.1376971
## [11,] 0.55696033 0.6344934
## [12,] 0.77840782 0.2585865
## [13,] 0.39993694 1.6319336
## [14,] 0.38387600 0.5104751
## [15,] 0.70338421 0.5375679
## [16,] 0.98799248 2.6060663
## [17,] 0.68667064 0.6208726
## [18,] 0.87453381 1.3576922
## [19,] 0.67518963 0.6541362
## [20,] 0.77324853 0.2665269
## [21,] 0.77704580 1.0394374
## [22,] 0.73599659 1.3133106
## [23,] 0.23274360 0.4443959
## [24,] 1.30336002 1.8999963
## [25,] 1.16014174 0.4038810
## [26,] 1.61473993 0.6060563
## [27,] 1.58917631 0.8940414
## [28,] 0.34141369 0.5217915
## [29,] 0.27440040 1.2435496
## [30,] 1.36481807 0.5366875
## [31,] 1.76876216 0.6281006
## [32,] 0.26026514 0.9878970
## [33,] 2.70423838 1.0036309
## [34,] 1.85116883 0.4887258
## [35,] 1.64174717 0.8959147
## [36,] 1.41627217 1.2222351
## [37,] 1.06043960 3.3711944
## [38,] 3.19918238 0.9379562
## [39,] 0.62223733 1.3685439
## [40,] 1.00443067 1.2619194
## [41,] 0.29222755 0.1809151
## [42,] 0.49577695 1.1309120
## [43,] 0.63298788 0.5052355
## [44,] 0.20842058 1.5482573
## [45,] 1.02952960 0.2508530
## [46,] 0.76120568 0.8428552
## [47,] 0.97881139 2.8360015
## [48,] 0.59632990 1.5377674
## [49,] 0.73912879 0.7019683
## [50,] 3.62777830 2.1098562
## [51,] 0.67789490 1.0268624
## [52,] 1.56635276 0.8085958
## [53,] 1.07717630 1.4393057
## [54,] 0.57284904 0.8766896
## [55,] 1.47364908 0.5751834
## [56,] 1.99679830 1.3605279
## [57,] 0.46284768 0.5414056
## [58,] 1.80964366 1.0383247
## [59,] 0.43285539 0.6481075
## [60,] 0.90260154 0.5838898
## [61,] 0.81583441 0.8773683
## [62,] 2.66610020 0.4439930
## [63,] 1.52262596 0.3348183
## [64,] 0.95908497 0.4279535
## [65,] 0.83322189 0.9099560
## [66,] 2.17042539 0.4409632
## [67,] 0.98106601 0.2831945
## [68,] 1.32170021 0.8207783
## [69,] 1.35296578 0.3469620
## [70,] 0.46540688 0.8476617
## [71,] 0.92988068 0.6207447
## [72,] 0.66447961 1.4579331
## [73,] 0.59033775 1.5648165
## [74,] 1.56707346 1.8551850
## [75,] 1.08302663 0.8570190
## [76,] 1.28780857 0.5925282
## [77,] 0.47891287 1.4337722
## [78,] 0.72002337 1.5214506
## [79,] 0.82007662 1.1019993
## [80,] 1.25132477 5.0408121
## [81,] 0.57207088 0.2251390
## [82,] 0.75058461 0.8654571
## [83,] 1.06139808 1.2438681
## [84,] 2.03029206 1.4827397
## [85,] 1.20743015 1.1488845
## [86,] 0.94166585 1.5039991
## [87,] 0.34567549 0.4143180
## [88,] 3.32316860 1.4847499
## [89,] 0.97011360 1.1405347
## [90,] 1.48016184 1.2849006
## [91,] 0.53846658 1.8824596
## [92,] 0.87245001 0.4209524
## [93,] 2.75490749 0.4885455
## [94,] 0.47310850 0.9284034
## [95,] 1.66909260 1.0563591
## [96,] 0.98590247 1.3061993
## [97,] 0.61341665 0.5225113
## [98,] 0.43471984 1.0161509
## [99,] 0.78170409 0.5183545
## [100,] 1.17846175 1.4244480
## [101,] 3.14040139 1.2148783
## [102,] 0.82253364 0.9332218
## [103,] 1.03786706 1.2280901
## [104,] 0.96841375 1.0440487
## [105,] 1.02109055 0.8431252
## [106,] 1.78561075 0.5405544
## [107,] 0.43958912 0.5622185
## [108,] 1.45605764 0.7206247
## [109,] 0.38607550 1.3717749
## [110,] 0.85132025 0.8787991
## [111,] 0.99777197 1.1206987
## [112,] 0.49657394 1.8428639
## [113,] 2.77406004 1.2672374
## [114,] 1.84414098 0.9328561
## [115,] 0.32837891 0.7242991
## [116,] 0.60293865 1.0657253
## [117,] 3.10031847 1.1562892
## [118,] 0.66070386 1.6633590
## [119,] 0.39632827 1.2714468
## [120,] 0.79697883 0.6175843
## [121,] 3.04106898 1.3577498
## [122,] 2.42038060 1.6231844
## [123,] 0.45759492 0.4011322
## [124,] 1.51959417 0.7126546
## [125,] 0.48602956 0.4905166
## [126,] 0.58136489 1.8686203
## [127,] 1.03320747 0.4874074
## [128,] 1.01303830 0.5042066
## [129,] 0.33139955 0.5710786
## [130,] 0.57022749 0.2781573
## [131,] 0.46715634 1.0847039
## [132,] 1.00581247 0.8984551
## [133,] 0.31740570 0.2978362
## [134,] 0.50637605 0.9242483
## [135,] 4.91207832 0.5961972
## [136,] 0.37728923 0.1759206
## [137,] 0.23045599 0.9242226
## [138,] 1.19776375 1.0393836
## [139,] 0.47885280 1.4791075
## [140,] 0.38346947 0.9248988
## [141,] 1.17265242 0.8799809
## [142,] 0.37691695 0.6131272
## [143,] 0.39583478 0.4792491
## [144,] 1.50591210 0.5281395
## [145,] 1.99846747 1.3059075
## [146,] 1.13521051 2.5073410
## [147,] 1.46813929 0.8832800
## [148,] 0.45426947 1.0309341
## [149,] 0.62955311 0.6645688
## [150,] 0.54952434 0.3568960
## [151,] 0.65907984 1.0565734
## [152,] 0.78490306 0.3501668
## [153,] 1.40852470 4.8610226
## [154,] 0.94346322 1.3021137
## [155,] 0.35697777 0.7316048
## [156,] 0.57503568 1.5090663
## [157,] 2.61281366 0.2097528
## [158,] 0.96943895 1.6850993
## [159,] 0.47376456 0.4818232
## [160,] 1.96506404 0.3202931
## [161,] 0.33223973 0.6106234
## [162,] 0.58890276 0.8652950
## [163,] 0.56662228 0.2961035
## [164,] 0.76447376 0.6709196
## [165,] 1.54581751 0.9364870
## [166,] 0.36154883 1.6331480
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## [631,] 0.70248138 0.6441429
## [632,] 1.19841839 0.8238524
## [633,] 0.42638481 1.1484546
## [634,] 1.24752097 0.8574953
## [635,] 0.68025214 0.4388239
## [636,] 0.30361330 0.9438826
## [637,] 0.65519324 1.7555445
## [638,] 0.51202536 2.1076696
## [639,] 1.09671517 0.3237157
## [640,] 0.06410089 0.6456168
## [641,] 1.28770211 0.9187439
## [642,] 0.45237349 1.0367428
## [643,] 1.51736784 1.2319641
## [644,] 1.47723830 1.5225434
## [645,] 0.74414493 0.7286364
## [646,] 0.54730097 1.2567242
## [647,] 0.50390316 1.4984220
## [648,] 0.59912692 1.3006924
## [649,] 0.91776438 3.1578943
## [650,] 1.13050017 2.4835308
## [651,] 1.36690891 0.8275713
## [652,] 0.28769551 0.4335568
## [653,] 1.45309772 1.1664955
## [654,] 0.48393192 0.1949628
## [655,] 2.32019470 1.8347398
## [656,] 2.91215789 2.4479782
## [657,] 0.64257869 1.1152602
## [658,] 0.61146883 0.8697240
## [659,] 0.54745071 0.8294598
## [660,] 0.45358316 0.9094684
## [661,] 1.86209223 1.0378279
## [662,] 0.31280313 0.6819495
## [663,] 0.99062940 0.5688196
## [664,] 0.58362834 1.4014366
## [665,] 1.29767475 0.8131148
## [666,] 0.56368661 0.3386420
## [667,] 1.06385174 1.1657908
## [668,] 1.80809713 0.3119592
## [669,] 0.34026775 0.1910332
## [670,] 0.44402039 1.5982165
## [671,] 1.63011924 1.8896159
## [672,] 1.12712054 2.8358096
## [673,] 0.68802672 1.7968997
## [674,] 0.84164999 1.2089139
## [675,] 0.17732828 0.6986172
## [676,] 0.81378405 0.3538056
## [677,] 0.24737165 0.8811370
## [678,] 1.24936104 0.2004780
## [679,] 1.64684394 0.4338029
## [680,] 0.37083646 0.4663276
## [681,] 0.87877279 0.6704607
## [682,] 0.84518757 0.4822439
## [683,] 0.60785272 1.8800410
## [684,] 1.15912474 1.5032783
## [685,] 1.14708780 0.9060396
## [686,] 2.43032897 1.8205028
## [687,] 0.62729858 0.5557188
## [688,] 1.17925745 0.2147091
## [689,] 0.43596949 0.4780095
## [690,] 1.33615549 0.7528482
## [691,] 1.00047034 1.3301800
## [692,] 0.65669788 0.5665422
## [693,] 0.47187980 0.3592098
## [694,] 3.00277785 0.4115450
## [695,] 1.28054376 1.3477040
## [696,] 0.23336416 0.6106067
## [697,] 0.87435723 0.5336155
## [698,] 0.86162839 0.7488722
## [699,] 1.68367652 1.2946814
## [700,] 0.17237899 1.0905128
## [701,] 0.74348733 0.5607427
## [702,] 1.40771273 1.0288495
## [703,] 0.79566112 0.4352144
## [704,] 0.92249555 0.4774840
## [705,] 1.08501238 1.5757038
## [706,] 0.75082305 3.5686703
## [707,] 0.57731478 0.4023920
## [708,] 0.94643188 0.5548729
## [709,] 1.33908230 1.4563204
## [710,] 1.11852426 0.9638878
## [711,] 1.51690307 2.2711093
## [712,] 0.96896613 1.0824847
## [713,] 1.90003341 0.3577276
## [714,] 1.34869392 1.3300138
## [715,] 1.75116271 2.1429531
## [716,] 1.10192045 0.7127888
## [717,] 1.92411804 1.1626389
## [718,] 0.95782382 1.3195086
## [719,] 0.46452066 1.0266345
## [720,] 2.33327670 0.7535329
## [721,] 0.70431961 0.4095754
## [722,] 0.61889482 0.5968625
## [723,] 2.54611203 1.1959639
## [724,] 0.69575971 0.5184066
## [725,] 0.55646625 0.7512479
## [726,] 0.63142744 1.2323042
## [727,] 1.31411681 0.4014105
## [728,] 1.46836050 3.7874602
## [729,] 3.87787566 0.2946527
## [730,] 2.07044077 0.2412865
## [731,] 0.31472390 0.8525800
## [732,] 1.19488888 3.6282645
## [733,] 0.77512006 1.0336877
## [734,] 1.54727412 1.1484797
## [735,] 0.76731719 4.8371090
## [736,] 0.90143834 0.8057870
## [737,] 0.60571514 0.5362546
## [738,] 0.66206677 0.9580251
## [739,] 1.25268583 0.7769230
## [740,] 0.69707197 0.4235283
## [741,] 1.90191685 0.2133243
## [742,] 0.75484078 1.5204905
## [743,] 0.86950891 0.6666057
## [744,] 1.25934808 0.9322101
## [745,] 2.20075944 1.5095102
## [746,] 1.61487831 0.4112771
## [747,] 0.72795970 0.7164128
## [748,] 2.16613421 0.9706847
## [749,] 0.42425513 0.9650048
## [750,] 0.82657824 0.5628484
## [751,] 1.25700522 0.8411519
## [752,] 0.58075094 0.7177401
## [753,] 1.56374970 0.8098214
## [754,] 0.12293582 1.6659026
## [755,] 0.72355632 0.6119912
## [756,] 0.64205408 0.5095687
## [757,] 1.91215703 0.6281910
## [758,] 2.36627290 1.1427033
## [759,] 0.29231523 1.0357593
## [760,] 1.93931725 1.1536789
## [761,] 1.34474488 1.1324691
## [762,] 0.40912285 0.2581767
## [763,] 0.58505554 0.6586129
## [764,] 0.53584804 0.2087720
## [765,] 0.49260728 0.3984549
## [766,] 0.86919870 0.5143073
## [767,] 0.61580808 0.5680969
## [768,] 2.15011608 0.6116784
## [769,] 0.33507967 0.5167507
## [770,] 0.48098977 0.4555907
## [771,] 0.29323257 0.3241618
## [772,] 0.43807658 0.5522117
## [773,] 0.59945977 1.2956945
## [774,] 0.64754053 0.8014917
## [775,] 4.30185599 0.3846354
## [776,] 0.43788330 1.0821829
## [777,] 0.22300111 0.6332761
## [778,] 0.22293963 0.8051476
## [779,] 0.58513127 0.8011112
## [780,] 1.59291443 0.5808460
## [781,] 0.58392704 1.1765356
## [782,] 0.17677251 0.8250374
## [783,] 0.56452957 2.1078476
## [784,] 0.30953936 1.1589478
## [785,] 0.99090843 1.1517757
## [786,] 0.98040959 2.0987666
## [787,] 0.23938546 0.9690597
## [788,] 0.77750479 0.8158301
## [789,] 0.37100569 1.0120035
## [790,] 3.01703720 0.7501027
## [791,] 1.06976902 1.7932978
## [792,] 1.30279937 0.7004976
## [793,] 1.59848449 0.7508667
## [794,] 0.80944427 0.2542158
## [795,] 0.88999117 0.9420539
## [796,] 0.52528486 0.5221366
## [797,] 0.50007397 0.3643280
## [798,] 0.82042810 0.6728878
## [799,] 0.78454906 1.1416047
## [800,] 0.58017452 0.8771815
## [801,] 0.58733497 1.9087884
## [802,] 0.62780148 0.8366287
## [803,] 0.53714647 0.4296790
## [804,] 1.06521888 1.0714702
## [805,] 1.85018006 1.0976054
## [806,] 0.47571805 1.0192515
## [807,] 0.84355869 0.2971449
## [808,] 0.83129419 1.2064371
## [809,] 2.18007347 1.1860421
## [810,] 0.62921261 1.7230174
## [811,] 0.89046462 1.1517023
## [812,] 0.79155056 0.7318432
## [813,] 2.97201729 1.2631694
## [814,] 0.40443798 2.2559094
## [815,] 2.16070158 1.0180115
## [816,] 2.20259456 1.1656339
## [817,] 0.98586925 0.6101497
## [818,] 1.01084350 1.0439240
## [819,] 0.99421000 0.8471173
## [820,] 1.42289198 1.5079117
## [821,] 1.77763822 0.2671525
## [822,] 0.72740517 0.9221009
## [823,] 1.34807090 0.8437444
## [824,] 1.97368030 0.4628382
## [825,] 1.87347928 0.1084476
## [826,] 0.77189029 0.6582549
## [827,] 0.51157048 0.8574418
## [828,] 0.61625527 0.4604354
## [829,] 0.33021038 1.2718445
## [830,] 1.05619982 0.9252157
## [831,] 0.41004443 0.2056754
## [832,] 0.43423724 0.3809702
## [833,] 0.34953049 0.9424481
## [834,] 0.52316591 1.0068131
## [835,] 0.48179467 0.3467477
## [836,] 0.72180482 0.9515771
## [837,] 0.51713112 0.6882767
## [838,] 1.04191204 0.5468752
## [839,] 1.72526810 0.6775245
## [840,] 1.35627011 0.1454602
## [841,] 0.98480166 0.9372470
## [842,] 1.07184983 0.3412724
## [843,] 0.71307595 1.4812772
## [844,] 1.73582392 1.0994799
## [845,] 0.81931911 0.5207496
## [846,] 0.68225064 1.1549170
## [847,] 2.39552530 0.3271115
## [848,] 1.14276671 0.5780169
## [849,] 0.15456494 0.4457840
## [850,] 0.72135799 0.8564635
## [851,] 1.27978110 0.9607251
## [852,] 0.75500830 0.7582362
## [853,] 1.12691139 0.4718323
## [854,] 0.66224421 1.3208272
## [855,] 1.15967096 0.8357068
## [856,] 1.29891517 1.1874824
## [857,] 0.70987749 0.6776459
## [858,] 0.72410982 0.7047933
## [859,] 0.59130087 1.1651493
## [860,] 0.79299956 0.4904724
## [861,] 0.43132159 0.4320980
## [862,] 0.21940303 0.9033722
## [863,] 3.64712935 1.3515488
## [864,] 1.20192997 0.2259046
## [865,] 0.82761195 1.5669602
## [866,] 0.64242190 0.3229068
## [867,] 1.04300250 1.0263216
## [868,] 0.76909200 0.7987564
## [869,] 0.47430078 0.8824073
## [870,] 1.20097867 0.9436169
## [871,] 0.75480514 0.8015523
## [872,] 2.07067423 0.5653110
## [873,] 1.01091984 1.0749982
## [874,] 0.74822830 0.2524207
## [875,] 0.50627477 1.4498846
## [876,] 3.67380311 0.3947072
## [877,] 0.61558279 0.4239835
## [878,] 2.02852571 0.4716817
## [879,] 3.00453875 0.5999413
## [880,] 0.47233078 0.9914937
## [881,] 1.19010198 1.8271512
## [882,] 0.84069588 0.5769016
## [883,] 1.21017329 1.9544810
## [884,] 1.45286238 2.6720118
## [885,] 0.50628462 0.4703280
## [886,] 3.33880529 0.6767213
## [887,] 0.42762127 0.7027358
## [888,] 0.54730047 1.0566285
## [889,] 1.28496402 1.6346632
## [890,] 2.59480104 0.5306793
## [891,] 0.87756229 3.0428468
## [892,] 1.91582978 0.5551037
## [893,] 0.40764128 1.1613811
## [894,] 0.46184048 0.4867443
## [895,] 0.51248911 0.5677282
## [896,] 0.44506752 0.4898592
## [897,] 2.84762507 2.1723468
## [898,] 0.66643576 0.6528615
## [899,] 0.62206285 0.3031422
## [900,] 1.21292638 0.5493861
## [901,] 0.99578030 0.6886083
## [902,] 0.76168383 0.4104400
## [903,] 1.93261775 0.4114208
## [904,] 0.69262381 0.4837668
## [905,] 0.43014873 0.8563557
## [906,] 0.66553174 0.6027835
## [907,] 1.41819254 0.3048096
## [908,] 0.79747221 1.5975816
## [909,] 1.83927012 0.8469353
## [910,] 0.66057521 0.7649361
## [911,] 0.81194740 0.6182865
## [912,] 0.67461520 1.1008965
## [913,] 1.88797929 0.9637572
## [914,] 0.77479496 0.8749445
## [915,] 0.68644519 0.9762334
## [916,] 0.86744229 0.8108681
## [917,] 0.93694062 0.6633495
## [918,] 0.63062012 1.2593403
## [919,] 1.26478804 2.0390032
## [920,] 1.25157351 0.5196868
## [921,] 0.35284311 0.4603766
## [922,] 0.78456296 0.4206922
## [923,] 0.32472507 0.4323342
## [924,] 0.25727667 1.6986068
## [925,] 1.70763546 2.0250919
## [926,] 0.67218346 0.9214438
## [927,] 0.70707210 0.5233351
## [928,] 0.50835619 0.5687120
## [929,] 0.86851381 0.8228832
## [930,] 2.10031865 0.6848575
## [931,] 0.26361145 0.6546123
## [932,] 0.98148208 0.2832739
## [933,] 2.39284956 1.4650066
## [934,] 0.04023172 1.0594909
## [935,] 0.95190734 1.9269836
## [936,] 0.45612927 0.7400490
## [937,] 0.39002183 0.4475721
## [938,] 0.70261876 1.6190117
## [939,] 0.77694349 1.1815229
## [940,] 0.92109829 0.5445483
## [941,] 0.84989611 0.7826478
## [942,] 0.77277748 0.6856594
## [943,] 0.62829219 0.7889788
## [944,] 0.43509812 0.9087642
## [945,] 1.14501891 1.5746326
## [946,] 0.76629638 0.6343912
## [947,] 0.93991113 0.1595323
## [948,] 0.93773796 1.6091783
## [949,] 0.87200184 1.1630009
## [950,] 1.33776264 1.1057329
## [951,] 0.73711583 0.8860939
## [952,] 0.44261755 0.9148329
## [953,] 0.71741567 1.3538343
## [954,] 0.21711328 0.4746137
## [955,] 0.82589914 1.4652563
## [956,] 0.61546484 0.7581856
## [957,] 2.55694374 0.9901904
## [958,] 0.48593468 1.6158228
## [959,] 0.29848584 1.1080153
## [960,] 0.39015486 0.9626104
## [961,] 0.29414862 0.5014723
## [962,] 0.55295930 1.2537841
## [963,] 1.76909985 0.8207305
## [964,] 0.70067870 2.2439625
## [965,] 2.97164954 0.6126692
## [966,] 0.34168400 0.8891289
## [967,] 0.56317001 0.7408331
## [968,] 2.15714643 1.1597120
## [969,] 0.65554662 0.8532540
## [970,] 1.29204481 0.2259971
## [971,] 0.48160159 0.6233436
## [972,] 1.57116442 1.5033480
## [973,] 1.02378447 0.7649779
## [974,] 0.44172995 2.7319639
## [975,] 0.69508738 0.8874048
## [976,] 0.53530281 1.0046028
## [977,] 0.86731373 0.8765445
## [978,] 0.55448186 0.3190288
## [979,] 0.91530582 0.4833298
## [980,] 0.43981035 0.8824495
## [981,] 1.34811785 2.8718457
## [982,] 3.73387358 0.6677432
## [983,] 0.83973527 0.9602228
## [984,] 1.82446389 0.2438962
## [985,] 1.20317198 0.6127901
## [986,] 1.28636216 0.1840904
## [987,] 0.31453472 0.7717767
## [988,] 1.26768606 1.0169165
## [989,] 2.29964320 0.6336563
## [990,] 0.92030976 1.9316230
## [991,] 1.11494021 0.6238103
## [992,] 0.77004246 1.0983543
## [993,] 1.39372409 0.7553803
## [994,] 0.67723414 0.8657639
## [995,] 0.34473828 0.9032336
## [996,] 0.81423396 2.2875280
## [997,] 0.49961996 1.1116194
## [998,] 0.85828440 1.4167456
## [999,] 0.57267189 0.3282864
##
## $model.matrix
## (Intercept) microsite1 shrub_density
## 1 1 1 11
## 2 1 1 12
## 3 1 -1 0
## 4 1 -1 0
## 5 1 1 11
## 6 1 1 10
## 7 1 -1 0
## 8 1 -1 0
## 9 1 1 14
## 10 1 1 13
## 11 1 -1 0
## 12 1 -1 0
## 13 1 1 11
## 14 1 1 11
## 15 1 -1 0
## 16 1 -1 0
## 17 1 1 10
## 18 1 1 11
## 19 1 1 11
## 20 1 1 10
## 21 1 -1 0
## 22 1 -1 0
## 23 1 -1 0
## 24 1 -1 0
##
## $terms
## pca_data_final ~ microsite * shrub_density
## attr(,"variables")
## list(pca_data_final, microsite, shrub_density)
## attr(,"factors")
## microsite shrub_density microsite:shrub_density
## pca_data_final 0 0 0
## microsite 1 0 1
## shrub_density 0 1 1
## attr(,"term.labels")
## [1] "microsite" "shrub_density"
## [3] "microsite:shrub_density"
## attr(,"order")
## [1] 1 1 2
## attr(,"intercept")
## [1] 1
## attr(,"response")
## [1] 1
## attr(,".Environment")
## <environment: R_GlobalEnv>
##
## attr(,"class")
## [1] "adonis"
dist_final <- vegdist(pca_data_final, species = "bray")
res_final <- pcoa(dist_final)
p02 <- as.data.frame(res_final$vectors)%>%
dplyr::select(Axis.1, Axis.2) %>%
bind_cols(env_final,.)
p02$microsite <- ifelse(p02$microsite == "Density", "Shrub", p02$microsite)
pcoa_final <- ggplot(p02, aes(Axis.1, Axis.2, group = microsite)) +
geom_point(aes(color = microsite)) +
geom_text(aes(label=plot), hjust = 0, vjust = 0, check_overlap = TRUE, nudge_x = 0.01)+
scale_color_brewer(palette = "Set1") + theme_classic() + theme(text = element_text(size = 12), panel.border = element_rect(color = "black", fill = NA, size = 1.5), axis.text = element_text(size = 10)) +
labs(color = "Microsite")
## Warning: The `size` argument of `element_rect()` is deprecated as of ggplot2 3.4.0.
## ℹ Please use the `linewidth` argument instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
pcoa_final <- pcoa_final + labs(x = "Shrub Density Gradient", y = "Community Composition") + scale_color_manual(
values = c("Shrub" = "#009900", "Open" = "#0066cc"), # Change color codes here
labels = c("Shrub" = "Shrub", "Open" = "Open")) + coord_fixed(ratio = 1)
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
pcoa_final
model020 <- betadisper(dist_final, env_final$microsite)
model020
##
## Homogeneity of multivariate dispersions
##
## Call: betadisper(d = dist_final, group = env_final$microsite)
##
## No. of Positive Eigenvalues: 17
## No. of Negative Eigenvalues: 6
##
## Average distance to median:
## Density Open
## 0.5034 0.4680
##
## Eigenvalues for PCoA axes:
## (Showing 8 of 23 eigenvalues)
## PCoA1 PCoA2 PCoA3 PCoA4 PCoA5 PCoA6 PCoA7 PCoA8
## 2.6390 1.2441 0.9185 0.4221 0.3745 0.2048 0.1861 0.1553
anova(model020)
## Analysis of Variance Table
##
## Response: Distances
## Df Sum Sq Mean Sq F value Pr(>F)
## Groups 1 0.00750 0.0074952 0.2553 0.6184
## Residuals 22 0.64591 0.0293594
permutest(model020,pairwise = TRUE, permutations = 99)
##
## Permutation test for homogeneity of multivariate dispersions
## Permutation: free
## Number of permutations: 99
##
## Response: Distances
## Df Sum Sq Mean Sq F N.Perm Pr(>F)
## Groups 1 0.00750 0.0074952 0.2553 99 0.68
## Residuals 22 0.64591 0.0293594
##
## Pairwise comparisons:
## (Observed p-value below diagonal, permuted p-value above diagonal)
## Density Open
## Density 0.67
## Open 0.6184
model020.HSD <- TukeyHSD(model020)
model020.HSD
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = distances ~ group, data = df)
##
## $group
## diff lwr upr p adj
## Open-Density -0.03534393 -0.1804147 0.1097268 0.6183989
boxplot(model020)
model030 <- betadisper(dist_final, env_final$shrub_density)
model030
##
## Homogeneity of multivariate dispersions
##
## Call: betadisper(d = dist_final, group = env_final$shrub_density)
##
## No. of Positive Eigenvalues: 17
## No. of Negative Eigenvalues: 6
##
## Average distance to median:
## 0 10 11 12 13 14
## 0.4680 0.3766 0.4847 0.0000 0.0000 0.0000
##
## Eigenvalues for PCoA axes:
## (Showing 8 of 23 eigenvalues)
## PCoA1 PCoA2 PCoA3 PCoA4 PCoA5 PCoA6 PCoA7 PCoA8
## 2.6390 1.2441 0.9185 0.4221 0.3745 0.2048 0.1861 0.1553
anova(model030)
## Analysis of Variance Table
##
## Response: Distances
## Df Sum Sq Mean Sq F value Pr(>F)
## Groups 5 0.58003 0.11601 2.233 0.09554 .
## Residuals 18 0.93511 0.05195
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
permutest(model030,pairwise = TRUE, permutations = 99)
##
## Permutation test for homogeneity of multivariate dispersions
## Permutation: free
## Number of permutations: 99
##
## Response: Distances
## Df Sum Sq Mean Sq F N.Perm Pr(>F)
## Groups 5 0.58003 0.11601 2.233 99 0.12
## Residuals 18 0.93511 0.05195
##
## Pairwise comparisons:
## (Observed p-value below diagonal, permuted p-value above diagonal)
## 0 10 11 12 13 14
## 0 0.54000 0.91000
## 10 0.55757 0.60000
## 11 0.86321 0.60937
## 12
## 13
## 14
model030.HSD <- TukeyHSD(model030)
boxplot(model030)
### Shows a significant difference between community composition of tested sites (Carrizo -> Cuyama -> Tecopa)
model040 <- betadisper(dist_final, env_final$site)
model040
##
## Homogeneity of multivariate dispersions
##
## Call: betadisper(d = dist_final, group = env_final$site)
##
## No. of Positive Eigenvalues: 17
## No. of Negative Eigenvalues: 6
##
## Average distance to median:
## Carrizo Cuyama Tecopa
## 0.2734 0.3349 0.4558
##
## Eigenvalues for PCoA axes:
## (Showing 8 of 23 eigenvalues)
## PCoA1 PCoA2 PCoA3 PCoA4 PCoA5 PCoA6 PCoA7 PCoA8
## 2.6390 1.2441 0.9185 0.4221 0.3745 0.2048 0.1861 0.1553
anova(model040)
## Analysis of Variance Table
##
## Response: Distances
## Df Sum Sq Mean Sq F value Pr(>F)
## Groups 2 0.13782 0.068911 3.7363 0.04091 *
## Residuals 21 0.38732 0.018444
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
permutest(model040,pairwise = TRUE, permutations = 99)
##
## Permutation test for homogeneity of multivariate dispersions
## Permutation: free
## Number of permutations: 99
##
## Response: Distances
## Df Sum Sq Mean Sq F N.Perm Pr(>F)
## Groups 2 0.13782 0.068911 3.7363 99 0.07 .
## Residuals 21 0.38732 0.018444
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Pairwise comparisons:
## (Observed p-value below diagonal, permuted p-value above diagonal)
## Carrizo Cuyama Tecopa
## Carrizo 0.420000 0.05
## Cuyama 0.425689 0.07
## Tecopa 0.016550 0.067329
model040.HSD <- TukeyHSD(model040)
supp_plot <- boxplot(model040, xlab = "Region")
### Temperature Data 2022
Temp_2022 <- read_csv("Temp Data 2022.csv")
## Rows: 52443 Columns: 12
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (5): researcher, site, site_code, microsite, time_block
## dbl (5): microsite_number, pendent_number, pendent_ID, rep, temp
## date (1): date
## time (1): time
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
Temp_2022_final <- Temp_2022 %>%
group_by(site_code, microsite) %>%
summarise(mean_temp = mean(temp), max_temp = max(temp))
## `summarise()` has grouped output by 'site_code'. You can override using the
## `.groups` argument.
###Ground Temp Data
Ground_Temp_2022 <- read_csv("Gradient Density Datasheet 2022.csv")
## Rows: 695 Columns: 26
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (4): site, site_code, date, microsite
## dbl (22): rep, microsite_number, shrub_ID, shrub_number, total_shrub, micros...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
Ground_Temp_2022_final <- Ground_Temp_2022 %>%
group_by(site_code, microsite) %>%
summarise(mean_ground_temp = mean(ground_temp), mean_humidity = mean(RH), max_humidity = max(RH), max_ground_temp = max(ground_temp))
## `summarise()` has grouped output by 'site_code'. You can override using the
## `.groups` argument.
### Combine this with new_data
### Aridity Data
aridity <- read_csv("regional_sites_2022.csv")
## Rows: 51 Columns: 25
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (17): state, desert, region, experiment, site_acronym, sub_site, site_co...
## dbl (7): lat, long, elevation, MAT, MAP, aridity, area_block_m2
## num (1): area_m2
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
aridity <- aridity %>%
dplyr::select(site_code, aridity)
final_2022<- photo%>%
group_by(site, site_code, year, microsite, plot, shrub_density, common_name) %>%
summarise(captures = sum(animal.hit), n = n())
## `summarise()` has grouped output by 'site', 'site_code', 'year', 'microsite',
## 'plot', 'shrub_density'. You can override using the `.groups` argument.
final_2022 <- final_2022 %>%
filter(common_name != "Blank")%>% filter(common_name != "No CV Result")
density_simple <- final_2022 %>%
group_by(site, site_code, year, microsite, plot, shrub_density) %>%
summarise(animals = sum(captures), richness = n()) %>%
rename(microsite_number = 'plot')
## `summarise()` has grouped output by 'site', 'site_code', 'year', 'microsite',
## 'plot'. You can override using the `.groups` argument.
#write.csv(density_simple, file = "density_simple_fixed.csv") Output density simple because it does not include the sites with 0 observations
density_simple_fixed <- read.csv("density_simple_fixed.csv")
density_simple_fixed <- density_simple_fixed[,-1]
### This is for 2022 evenness
vegan_data <- animals_density ### Created new df for pca data
vegan_data <- vegan_data %>%
spread(common_name, captures) %>%
ungroup() %>%
replace(is.na(.),0)
evenness_data <- vegan_data %>%
group_by(site_code, microsite) %>%
summarize(across(5:29, ~diversity(., index = "shannon")))
## `summarise()` has grouped output by 'site_code'. You can override using the
## `.groups` argument.
evenness_data <- na.omit(evenness_data)
evenness_data <- evenness_data %>%
mutate(Average_Evenness = rowMeans(across(5:26)))
evenness_data <- evenness_data %>%
dplyr::select(site_code, microsite, Average_Evenness)
### Join evenness_data with density_simple data
new_data <- inner_join(density_simple_fixed, evenness_data, by = c("site_code", "microsite"))
### Combine new data with logger temp data from 2022
new_data <- inner_join(new_data, Temp_2022_final,by = c("site_code", "microsite"))
### Need to add ground temperature from hand recordings then 2022 is ready!
new_data <- inner_join(new_data, Ground_Temp_2022_final, by = c("site_code", "microsite"))
### Combine all data with aridity data of the sites we have
new_data <- inner_join(new_data, aridity, by = c("site_code"))
### 2022 data is now cleaned and ready to go
### Clean up 2023 data so it can be properly combined with 2022 data
final_2023<- photo_2023%>%
group_by(site, site_code, year, microsite, plot, shrub_density, common_name) %>%
summarise(captures = sum(animal.hit), n = n())
## `summarise()` has grouped output by 'site', 'site_code', 'year', 'microsite',
## 'plot', 'shrub_density'. You can override using the `.groups` argument.
density_simple_2023 <- final_2023 %>%
group_by(site, site_code, year, microsite, plot, shrub_density) %>%
summarise(animals = sum(captures), richness = n()) %>%
rename(microsite_number = 'plot')
## `summarise()` has grouped output by 'site', 'site_code', 'year', 'microsite',
## 'plot'. You can override using the `.groups` argument.
write.csv(density_simple_2023, file = "density_simple_fixed_2023.csv")
### This is for 2023 evenness
vegan_data_2023 <- animals_density_2023 ### Created new df for pca data
vegan_data_2023 <- vegan_data_2023 %>%
spread(common_name, captures) %>%
ungroup() %>%
replace(is.na(.),0)
evenness_data_2023 <- vegan_data_2023 %>%
group_by(site_code, microsite) %>%
summarize(across(5:27, ~diversity(., index = "shannon")))
## `summarise()` has grouped output by 'site_code'. You can override using the
## `.groups` argument.
evenness_data_2023 <- na.omit(evenness_data_2023)
evenness_data_2023 <- evenness_data_2023 %>%
mutate(Average_Evenness = rowMeans(across(5:24)))
evenness_data_2023 <- evenness_data_2023 %>%
dplyr::select(site_code, microsite, Average_Evenness)
new_data_2023 <- inner_join(density_simple_2023, evenness_data_2023, by = c("site_code", "microsite"))
new_data_2023$site_code <- gsub("Tecopa_Shrub", "Tecopa_shrub", new_data_2023$site_code)
new_data_2023$site_code <- gsub("Tecopa_Open", "Tecopa_open", new_data_2023$site_code)
### Follow same steps as above 2022 data clean up. Start with logger temp, then ground temp, then aridity.
### Combine new_data_2023 to Temp_2023
Temp_2023 <- read_csv("Temp Data 2023.csv") %>% filter(!(temp > 50)) %>% filter(!(temp < -47))
## Rows: 24760 Columns: 11
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (4): researcher, site, site_code, microsite
## dbl (5): microsite_number, pendant_number, pendant_ID, rep, temp
## date (1): date
## time (1): time
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
Temp_2023_final <- Temp_2023 %>%
group_by(site_code, microsite) %>%
summarise(mean_temp = mean(temp), max_temp = max(temp))
## `summarise()` has grouped output by 'site_code'. You can override using the
## `.groups` argument.
new_data_2023 <- inner_join(new_data_2023, Temp_2023_final, by = c("site_code", "microsite"))
### Combine Ground Temp Data from 2023
Ground_Temp_2023 <- read_csv("Gradient Density Datasheet 2023.csv")
## New names:
## Rows: 587 Columns: 29
## ── Column specification
## ──────────────────────────────────────────────────────── Delimiter: "," chr
## (4): site, site_code, date, microsite dbl (25): rep, microsite_number,
## shrub_ID, shrub_number, total_shrub, micros...
## ℹ Use `spec()` to retrieve the full column specification for this data. ℹ
## Specify the column types or set `show_col_types = FALSE` to quiet this message.
## • `` -> `...28`
## • `` -> `...29`
Ground_Temp_2023_final <- Ground_Temp_2023 %>%
group_by(site_code, microsite, microsite_number) %>%
summarise(mean_ground_temp = mean(ground_temp), mean_humidity = mean(RH), max_humidity = max(RH), max_ground_temp = max(ground_temp))
## `summarise()` has grouped output by 'site_code', 'microsite'. You can override
## using the `.groups` argument.
new_data_2023 <- inner_join(new_data_2023, Ground_Temp_2023_final, by = c("site_code", "microsite", "microsite_number"))
### Aridity data is already set up from 2022 data
new_data_2023 <- inner_join(new_data_2023, aridity, by = c("site_code"))
### 2023 Data is now cleaned and ready to be combined with 2022 data
final_data <- rbind(new_data, new_data_2023)
library(ggpubr)
shapiro.test(final_data$animals)
##
## Shapiro-Wilk normality test
##
## data: final_data$animals
## W = 0.62948, p-value = 1.551e-09
ggqqplot(final_data$animals)
### Stats to show that the aridity across tested regions significantly varies. This needs to be one of the FIRST things you put in your results.
anova_result <- aov(aridity ~ site, data = final_data)
summary(anova_result)
## Df Sum Sq Mean Sq F value Pr(>F)
## site 2 103.52 51.76 3639 <2e-16 ***
## Residuals 43 0.61 0.01
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
emmeans(anova_result, pairwise ~ site)
## $emmeans
## site emmean SE df lower.CL upper.CL
## Carrizo 3.252 0.0319 43 3.188 3.316
## Cuyama 3.701 0.0298 43 3.641 3.762
## Tecopa 0.365 0.0298 43 0.305 0.425
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## Carrizo - Cuyama -0.449 0.0436 43 -10.297 <.0001
## Carrizo - Tecopa 2.887 0.0436 43 66.147 <.0001
## Cuyama - Tecopa 3.336 0.0422 43 79.126 <.0001
##
## P value adjustment: tukey method for comparing a family of 3 estimates
tukey_result <- TukeyHSD(anova_result)
# View the Tukey's HSD results
print(tukey_result)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = aridity ~ site, data = final_data)
##
## $site
## diff lwr upr p adj
## Cuyama-Carrizo 0.4494073 0.3434585 0.5553561 0
## Tecopa-Carrizo -2.8870527 -2.9930014 -2.7811039 0
## Tecopa-Cuyama -3.3364600 -3.4388162 -3.2341037 0
### Tukey test shows Cuyama less arid than Carrizo, Carrizo less arid than Tecopa, Cuyama less arid than Tecopa
### Abundance
model1 <- glm(animals~shrub_density*site*year+aridity, family = "gaussian", data = final_data)
model1
##
## Call: glm(formula = animals ~ shrub_density * site * year + aridity,
## family = "gaussian", data = final_data)
##
## Coefficients:
## (Intercept) shrub_density
## 7.551e+04 1.061e+04
## siteCuyama siteTecopa
## 4.027e+05 -9.065e+04
## year aridity
## -3.736e+01 3.297e+01
## shrub_density:siteCuyama shrub_density:siteTecopa
## -1.559e+03 -1.211e+04
## shrub_density:year siteCuyama:year
## -5.245e+00 -1.991e+02
## siteTecopa:year shrub_density:siteCuyama:year
## 4.485e+01 7.703e-01
## shrub_density:siteTecopa:year
## 5.986e+00
##
## Degrees of Freedom: 45 Total (i.e. Null); 33 Residual
## Null Deviance: 765800
## Residual Deviance: 302900 AIC: 563
anova(model1, test = "Chisq")
## Analysis of Deviance Table
##
## Model: gaussian, link: identity
##
## Response: animals
##
## Terms added sequentially (first to last)
##
##
## Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL 45 765773
## shrub_density 1 18242 44 747531 0.1586319
## site 2 139832 42 607700 0.0004923 ***
## year 1 137687 41 470013 0.0001076 ***
## aridity 1 55 40 469958 0.9380678
## shrub_density:site 2 3544 38 466414 0.8244495
## shrub_density:year 1 5580 37 460833 0.4355873
## site:year 2 155562 35 305272 0.0002090 ***
## shrub_density:site:year 2 2342 33 302929 0.8802245
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
e2 <- emmeans(model1, pairwise~site*year)
## NOTE: Results may be misleading due to involvement in interactions
e2
## $emmeans
## site year emmean SE df lower.CL upper.CL
## Carrizo 2022 83.8 122 33 -164 332
## Cuyama 2022 235.1 176 33 -123 593
## Tecopa 2022 74.6 274 33 -482 631
## Carrizo 2023 15.7 111 33 -210 242
## Cuyama 2023 -27.6 176 33 -386 330
## Tecopa 2023 86.4 274 33 -470 643
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## Carrizo year2022 - Cuyama year2022 -151.28 73.5 33 -2.057 0.3336
## Carrizo year2022 - Tecopa year2022 9.25 391.4 33 0.024 1.0000
## Carrizo year2022 - Carrizo year2023 68.15 54.7 33 1.246 0.8109
## Carrizo year2022 - Cuyama year2023 111.45 73.5 33 1.516 0.6570
## Carrizo year2022 - Tecopa year2023 -2.59 391.4 33 -0.007 1.0000
## Cuyama year2022 - Tecopa year2022 160.53 446.7 33 0.359 0.9991
## Cuyama year2022 - Carrizo year2023 219.43 87.3 33 2.515 0.1490
## Cuyama year2022 - Cuyama year2023 262.73 47.9 33 5.480 0.0001
## Cuyama year2022 - Tecopa year2023 148.70 446.7 33 0.333 0.9994
## Tecopa year2022 - Carrizo year2023 58.90 378.5 33 0.156 1.0000
## Tecopa year2022 - Cuyama year2023 102.20 446.7 33 0.229 0.9999
## Tecopa year2022 - Tecopa year2023 -11.83 48.2 33 -0.245 0.9999
## Carrizo year2023 - Cuyama year2023 43.30 87.3 33 0.496 0.9960
## Carrizo year2023 - Tecopa year2023 -70.73 378.5 33 -0.187 1.0000
## Cuyama year2023 - Tecopa year2023 -114.04 446.7 33 -0.255 0.9998
##
## P value adjustment: tukey method for comparing a family of 6 estimates
shapiro.test(final_data$richness)
##
## Shapiro-Wilk normality test
##
## data: final_data$richness
## W = 0.96052, p-value = 0.1201
ggqqplot(final_data$richness)
### Richness Stats
model2 <- glm(richness~shrub_density*site*year +aridity, family = "gaussian", data = final_data)
model2
##
## Call: glm(formula = richness ~ shrub_density * site * year + aridity,
## family = "gaussian", data = final_data)
##
## Coefficients:
## (Intercept) shrub_density
## -1.049e+03 -9.984e+01
## siteCuyama siteTecopa
## 1.799e+04 -6.515e+03
## year aridity
## 5.227e-01 -5.391e-01
## shrub_density:siteCuyama shrub_density:siteTecopa
## -2.708e+02 -1.922e+02
## shrub_density:year siteCuyama:year
## 4.939e-02 -8.895e+00
## siteTecopa:year shrub_density:siteCuyama:year
## 3.219e+00 1.339e-01
## shrub_density:siteTecopa:year
## 9.508e-02
##
## Degrees of Freedom: 45 Total (i.e. Null); 33 Residual
## Null Deviance: 575.9
## Residual Deviance: 79.32 AIC: 183.6
anova_results2 <- aov(model2, test = "Chisq")
## Warning: In lm.fit(x, y, offset = offset, singular.ok = singular.ok, ...) :
## extra argument 'test' will be disregarded
anova(model2, test = "Chisq")
## Analysis of Deviance Table
##
## Model: gaussian, link: identity
##
## Response: richness
##
## Terms added sequentially (first to last)
##
##
## Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL 45 575.91
## shrub_density 1 27.132 44 548.78 0.0007803 ***
## site 2 164.760 42 384.02 1.305e-15 ***
## year 1 6.218 41 377.80 0.1077656
## aridity 1 1.307 40 376.50 0.4609209
## shrub_density:site 2 1.941 38 374.56 0.6678024
## shrub_density:year 1 3.261 37 371.29 0.2441039
## site:year 2 290.938 35 80.36 < 2.2e-16 ***
## shrub_density:site:year 2 1.034 33 79.32 0.8064261
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(anova_results2)
## Df Sum Sq Mean Sq F value Pr(>F)
## shrub_density 1 27.13 27.13 11.287 0.00198 **
## site 2 164.76 82.38 34.272 8.82e-09 ***
## year 1 6.22 6.22 2.587 0.11729
## aridity 1 1.31 1.31 0.544 0.46613
## shrub_density:site 2 1.94 0.97 0.404 0.67106
## shrub_density:year 1 3.26 3.26 1.357 0.25246
## site:year 2 290.94 145.47 60.519 9.11e-12 ***
## shrub_density:site:year 2 1.03 0.52 0.215 0.80755
## Residuals 33 79.32 2.40
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
e3 <- emmeans(model2, pairwise~site*year)
## NOTE: Results may be misleading due to involvement in interactions
e3 ### Ok now this all works.
## $emmeans
## site year emmean SE df lower.CL upper.CL
## Carrizo 2022 6.356 1.97 33 2.346 10.37
## Cuyama 2022 12.443 2.85 33 6.649 18.24
## Tecopa 2022 0.208 4.43 33 -8.799 9.21
## Carrizo 2023 7.168 1.80 33 3.513 10.82
## Cuyama 2023 5.146 2.85 33 -0.648 10.94
## Tecopa 2023 4.797 4.43 33 -4.209 13.80
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## Carrizo year2022 - Cuyama year2022 -6.087 1.190 33 -5.116 0.0002
## Carrizo year2022 - Tecopa year2022 6.148 6.333 33 0.971 0.9239
## Carrizo year2022 - Carrizo year2023 -0.813 0.885 33 -0.918 0.9390
## Carrizo year2022 - Cuyama year2023 1.210 1.190 33 1.017 0.9090
## Carrizo year2022 - Tecopa year2023 1.558 6.333 33 0.246 0.9999
## Cuyama year2022 - Tecopa year2022 12.235 7.229 33 1.692 0.5461
## Cuyama year2022 - Carrizo year2023 5.274 1.412 33 3.736 0.0085
## Cuyama year2022 - Cuyama year2023 7.297 0.776 33 9.405 <.0001
## Cuyama year2022 - Tecopa year2023 7.645 7.229 33 1.058 0.8944
## Tecopa year2022 - Carrizo year2023 -6.960 6.125 33 -1.136 0.8626
## Tecopa year2022 - Cuyama year2023 -4.938 7.229 33 -0.683 0.9826
## Tecopa year2022 - Tecopa year2023 -4.590 0.781 33 -5.880 <.0001
## Carrizo year2023 - Cuyama year2023 2.022 1.412 33 1.432 0.7076
## Carrizo year2023 - Tecopa year2023 2.371 6.125 33 0.387 0.9988
## Cuyama year2023 - Tecopa year2023 0.348 7.229 33 0.048 1.0000
##
## P value adjustment: tukey method for comparing a family of 6 estimates
ggqqplot(final_data$Average_Evenness)
### Evenness
model3 <- glm(Average_Evenness~shrub_density*site*year + aridity, family = "gaussian", data = final_data)
model3
##
## Call: glm(formula = Average_Evenness ~ shrub_density * site * year +
## aridity, family = "gaussian", data = final_data)
##
## Coefficients:
## (Intercept) shrub_density
## 1.175e+02 -8.606e+00
## siteCuyama siteTecopa
## 2.013e+02 -3.866e+02
## year aridity
## -5.801e-02 -2.568e-02
## shrub_density:siteCuyama shrub_density:siteTecopa
## 1.008e+01 -9.806e+00
## shrub_density:year siteCuyama:year
## 4.257e-03 -9.952e-02
## siteTecopa:year shrub_density:siteCuyama:year
## 1.911e-01 -4.981e-03
## shrub_density:siteTecopa:year
## 4.848e-03
##
## Degrees of Freedom: 45 Total (i.e. Null); 33 Residual
## Null Deviance: 0.3064
## Residual Deviance: 0.02358 AIC: -190
anova_results3 <- aov(model3, test = "Chisq")
## Warning: In lm.fit(x, y, offset = offset, singular.ok = singular.ok, ...) :
## extra argument 'test' will be disregarded
anova(model3, test = "Chisq")
## Analysis of Deviance Table
##
## Model: gaussian, link: identity
##
## Response: Average_Evenness
##
## Terms added sequentially (first to last)
##
##
## Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL 45 0.306353
## shrub_density 1 0.020430 44 0.285923 8.936e-08 ***
## site 2 0.011457 42 0.274466 0.0003297 ***
## year 1 0.000058 41 0.274408 0.7763951
## aridity 1 0.000022 40 0.274386 0.8618492
## shrub_density:site 2 0.001475 38 0.272911 0.3562435
## shrub_density:year 1 0.000932 37 0.271980 0.2535206
## site:year 2 0.242134 35 0.029845 < 2.2e-16 ***
## shrub_density:site:year 2 0.006265 33 0.023580 0.0124756 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(anova_results3)
## Df Sum Sq Mean Sq F value Pr(>F)
## shrub_density 1 0.02043 0.02043 28.592 6.62e-06 ***
## site 2 0.01146 0.00573 8.017 0.00145 **
## year 1 0.00006 0.00006 0.081 0.77817
## aridity 1 0.00002 0.00002 0.030 0.86291
## shrub_density:site 2 0.00148 0.00074 1.032 0.36746
## shrub_density:year 1 0.00093 0.00093 1.304 0.26174
## site:year 2 0.24213 0.12107 169.432 < 2e-16 ***
## shrub_density:site:year 2 0.00627 0.00313 4.384 0.02049 *
## Residuals 33 0.02358 0.00071
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
e4 <- emmeans(model3, pairwise~site*year)
## NOTE: Results may be misleading due to involvement in interactions
e4 ### Need to double check emmeans it does not match the figure
## $emmeans
## site year emmean SE df lower.CL upper.CL
## Carrizo 2022 0.1186 0.0340 33 0.04944 0.188
## Cuyama 2022 0.2302 0.0491 33 0.13028 0.330
## Tecopa 2022 -0.0251 0.0763 33 -0.18038 0.130
## Carrizo 2023 0.0856 0.0310 33 0.02254 0.149
## Cuyama 2023 0.0684 0.0491 33 -0.03150 0.168
## Tecopa 2023 0.1615 0.0763 33 0.00617 0.317
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## Carrizo year2022 - Cuyama year2022 -0.1116 0.0205 33 -5.440 0.0001
## Carrizo year2022 - Tecopa year2022 0.1437 0.1092 33 1.316 0.7744
## Carrizo year2022 - Carrizo year2023 0.0330 0.0153 33 2.165 0.2809
## Carrizo year2022 - Cuyama year2023 0.0502 0.0205 33 2.446 0.1701
## Carrizo year2022 - Tecopa year2023 -0.0429 0.1092 33 -0.393 0.9987
## Cuyama year2022 - Tecopa year2022 0.2553 0.1246 33 2.048 0.3384
## Cuyama year2022 - Carrizo year2023 0.1446 0.0243 33 5.941 <.0001
## Cuyama year2022 - Cuyama year2023 0.1618 0.0134 33 12.094 <.0001
## Cuyama year2022 - Tecopa year2023 0.0687 0.1246 33 0.551 0.9934
## Tecopa year2022 - Carrizo year2023 -0.1106 0.1056 33 -1.048 0.8980
## Tecopa year2022 - Cuyama year2023 -0.0935 0.1246 33 -0.750 0.9738
## Tecopa year2022 - Tecopa year2023 -0.1865 0.0135 33 -13.861 <.0001
## Carrizo year2023 - Cuyama year2023 0.0172 0.0243 33 0.705 0.9800
## Carrizo year2023 - Tecopa year2023 -0.0759 0.1056 33 -0.719 0.9782
## Cuyama year2023 - Tecopa year2023 -0.0931 0.1246 33 -0.747 0.9743
##
## P value adjustment: tukey method for comparing a family of 6 estimates
### All below in chunk are by site
### Abundance vs Density
abundance <- ggplot(final_data, aes(shrub_density, animals, color = site)) +
geom_point(size = 0.5) +
facet_wrap(~year)+
scale_color_brewer(palette = "Set1") + theme_classic() + theme(legend.position = "none") + theme(text = element_text(size = 12), panel.border = element_rect(color = "black", fill = NA, size = 1.5),legend.position = "none", legend.title = element_blank(), axis.text = element_text(size = 10)) +
geom_smooth(method = lm, se = TRUE) + theme(axis.title.x = element_blank()) + labs(tag = "A")+
labs(x = "Shrub Density (Individuals per 20m radius)", y = "Animal Abundance")
abundance
## `geom_smooth()` using formula = 'y ~ x'
richness <- ggplot(final_data, aes(shrub_density, richness, color = site)) +
geom_point(size = 0.5) +
facet_wrap(~year)+
scale_color_brewer(palette = "Set1") + theme_classic() + theme(text = element_text(size = 12), panel.border = element_rect(color = "black", fill = NA, size = 1.5), axis.text = element_text(size = 10)) +
geom_smooth(method = lm, se = TRUE) + theme(axis.title.x = element_blank()) + labs(tag = "B")+
labs(x = "Shrub Density (Individuals per 20m radius)", y = "Richness", color = "Region")
richness
## `geom_smooth()` using formula = 'y ~ x'
Evenness <- ggplot(final_data, aes(shrub_density, Average_Evenness, color = site)) +
geom_point(size = 0.5) +
facet_wrap(~year)+
scale_color_brewer(palette = "Set1") + theme_classic() + theme(legend.position = "none") + theme(text = element_text(size = 12), panel.border = element_rect(color = "black", fill = NA, size = 1.5),legend.position = "none", legend.title = element_blank(), axis.text = element_text(size = 10)) +
geom_smooth(method = lm, se = TRUE) + labs(tag = "C")+
labs(x = expression("Shrub Density per " * 20 * m^2), y = "Mean Evenness")
Evenness
## `geom_smooth()` using formula = 'y ~ x'
library(patchwork)
##
## Attaching package: 'patchwork'
##
## The following object is masked from 'package:MASS':
##
## area
density_plot <- abundance/richness/Evenness
density_plot
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
### Stats for Average Temperature
### Abundance and temperature
model4 <- glm(animals~mean_temp*site*year*mean_humidity, family = "gaussian", data = final_data)
model4
##
## Call: glm(formula = animals ~ mean_temp * site * year * mean_humidity,
## family = "gaussian", data = final_data)
##
## Coefficients:
## (Intercept)
## -6.423e+08
## mean_temp
## 2.677e+07
## siteCuyama
## 4.148e+08
## siteTecopa
## -4.661e+08
## year
## 3.175e+05
## mean_humidity
## 2.602e+07
## mean_temp:siteCuyama
## -1.626e+07
## mean_temp:siteTecopa
## 1.146e+07
## mean_temp:year
## -1.323e+04
## siteCuyama:year
## -2.051e+05
## siteTecopa:year
## 2.304e+05
## mean_temp:mean_humidity
## -1.084e+06
## siteCuyama:mean_humidity
## -1.764e+07
## siteTecopa:mean_humidity
## -7.572e+02
## year:mean_humidity
## -1.286e+04
## mean_temp:siteCuyama:year
## 8.037e+03
## mean_temp:siteTecopa:year
## -5.664e+03
## mean_temp:siteCuyama:mean_humidity
## 6.983e+05
## mean_temp:siteTecopa:mean_humidity
## 2.925e+01
## mean_temp:year:mean_humidity
## 5.360e+02
## siteCuyama:year:mean_humidity
## 8.722e+03
## siteTecopa:year:mean_humidity
## NA
## mean_temp:siteCuyama:year:mean_humidity
## -3.452e+02
## mean_temp:siteTecopa:year:mean_humidity
## NA
##
## Degrees of Freedom: 45 Total (i.e. Null); 24 Residual
## Null Deviance: 765800
## Residual Deviance: 57540 AIC: 504.6
anova_results4 <- aov(model4, test = "Chisq")
## Warning: In lm.fit(x, y, offset = offset, singular.ok = singular.ok, ...) :
## extra argument 'test' will be disregarded
anova(model4, test = "Chisq")
## Analysis of Deviance Table
##
## Model: gaussian, link: identity
##
## Response: animals
##
## Terms added sequentially (first to last)
##
##
## Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL 45 765773
## mean_temp 1 69722 44 696052 6.934e-08
## site 2 82375 42 613676 3.456e-08
## year 1 184771 41 428905 < 2.2e-16
## mean_humidity 1 1138 40 427767 0.4907466
## mean_temp:site 2 108585 38 319182 1.461e-10
## mean_temp:year 1 7837 37 311345 0.0706052
## site:year 2 33153 35 278192 0.0009931
## mean_temp:mean_humidity 1 962 34 277230 0.5264108
## site:mean_humidity 2 5743 32 271487 0.3018453
## year:mean_humidity 1 7165 31 264322 0.0838464
## mean_temp:site:year 2 6207 29 258114 0.2739932
## mean_temp:site:mean_humidity 2 160588 27 97526 2.845e-15
## mean_temp:year:mean_humidity 1 367 26 97159 0.6956019
## site:year:mean_humidity 1 36297 25 60862 9.978e-05
## mean_temp:site:year:mean_humidity 1 3326 24 57536 0.2388432
##
## NULL
## mean_temp ***
## site ***
## year ***
## mean_humidity
## mean_temp:site ***
## mean_temp:year .
## site:year ***
## mean_temp:mean_humidity
## site:mean_humidity
## year:mean_humidity .
## mean_temp:site:year
## mean_temp:site:mean_humidity ***
## mean_temp:year:mean_humidity
## site:year:mean_humidity ***
## mean_temp:site:year:mean_humidity
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(anova_results4)
## Df Sum Sq Mean Sq F value Pr(>F)
## mean_temp 1 69722 69722 28.639 1.50e-05 ***
## site 2 82375 41188 16.919 2.26e-05 ***
## year 1 184771 184771 75.898 4.80e-09 ***
## mean_humidity 1 1138 1138 0.468 0.500364
## mean_temp:site 2 108585 54292 22.301 2.76e-06 ***
## mean_temp:year 1 7837 7837 3.219 0.084892 .
## site:year 2 33153 16577 6.809 0.004358 **
## mean_temp:mean_humidity 1 962 962 0.395 0.535285
## site:mean_humidity 2 5743 2872 1.180 0.323946
## year:mean_humidity 1 7165 7165 2.943 0.098614 .
## mean_temp:site:year 2 6207 3104 1.275 0.297010
## mean_temp:site:mean_humidity 2 160588 80294 32.982 9.74e-08 ***
## mean_temp:year:mean_humidity 1 367 367 0.151 0.701105
## site:year:mean_humidity 1 36297 36297 14.910 0.000707 ***
## Residuals 25 60862 2434
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
e5 <- emmeans(model4, pairwise~site*year)
## NOTE: Results may be misleading due to involvement in interactions
e5
## $emmeans
## site year emmean SE df lower.CL upper.CL
## Carrizo 2022 -938.5 619 24 -2217 340
## Cuyama 2022 1714.0 146 24 1414 2015
## Tecopa 2022 -89833.5 55883 24 -205170 25503
## Carrizo 2023 37.5 261 24 -501 576
## Cuyama 2023 1006.2 1997 24 -3116 5128
## Tecopa 2023 -81.9 3323 24 -6940 6776
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## Carrizo year2022 - Cuyama year2022 -2652 636 24 -4.170 0.0041
## Carrizo year2022 - Tecopa year2022 88895 55335 24 1.606 0.6024
## Carrizo year2022 - Carrizo year2023 -976 672 24 -1.453 0.6959
## Carrizo year2022 - Cuyama year2023 -1945 2091 24 -0.930 0.9347
## Carrizo year2022 - Tecopa year2023 -857 3380 24 -0.253 0.9998
## Cuyama year2022 - Tecopa year2022 91548 55883 24 1.638 0.5828
## Cuyama year2022 - Carrizo year2023 1676 299 24 5.615 0.0001
## Cuyama year2022 - Cuyama year2023 708 2003 24 0.353 0.9992
## Cuyama year2022 - Tecopa year2023 1796 3326 24 0.540 0.9938
## Tecopa year2022 - Carrizo year2023 -89871 55876 24 -1.608 0.6012
## Tecopa year2022 - Cuyama year2023 -90840 55919 24 -1.624 0.5913
## Tecopa year2022 - Tecopa year2023 -89752 57406 24 -1.563 0.6288
## Carrizo year2023 - Cuyama year2023 -969 2014 24 -0.481 0.9964
## Carrizo year2023 - Tecopa year2023 119 3333 24 0.036 1.0000
## Cuyama year2023 - Tecopa year2023 1088 3877 24 0.281 0.9997
##
## P value adjustment: tukey method for comparing a family of 6 estimates
### Richness and temperature
model5 <- glm(richness~mean_temp*site*year*mean_humidity, family = "gaussian", data = final_data)
model5
##
## Call: glm(formula = richness ~ mean_temp * site * year * mean_humidity,
## family = "gaussian", data = final_data)
##
## Coefficients:
## (Intercept)
## 4.250e+05
## mean_temp
## -2.300e+04
## siteCuyama
## 8.940e+06
## siteTecopa
## 1.097e+05
## year
## -2.101e+02
## mean_humidity
## -1.721e+04
## mean_temp:siteCuyama
## -3.999e+05
## mean_temp:siteTecopa
## 5.848e+03
## mean_temp:year
## 1.137e+01
## siteCuyama:year
## -4.421e+03
## siteTecopa:year
## -5.411e+01
## mean_temp:mean_humidity
## 9.181e+02
## siteCuyama:mean_humidity
## -2.453e+05
## siteTecopa:mean_humidity
## 4.210e+00
## year:mean_humidity
## 8.507e+00
## mean_temp:siteCuyama:year
## 1.977e+02
## mean_temp:siteTecopa:year
## -2.896e+00
## mean_temp:siteCuyama:mean_humidity
## 1.097e+04
## mean_temp:siteTecopa:mean_humidity
## -3.693e-02
## mean_temp:year:mean_humidity
## -4.539e-01
## siteCuyama:year:mean_humidity
## 1.213e+02
## siteTecopa:year:mean_humidity
## NA
## mean_temp:siteCuyama:year:mean_humidity
## -5.424e+00
## mean_temp:siteTecopa:year:mean_humidity
## NA
##
## Degrees of Freedom: 45 Total (i.e. Null); 24 Residual
## Null Deviance: 575.9
## Residual Deviance: 21.07 AIC: 140.6
anova_results5 <- aov(model5, test = "Chisq")
## Warning: In lm.fit(x, y, offset = offset, singular.ok = singular.ok, ...) :
## extra argument 'test' will be disregarded
anova(model5, test = "Chisq")
## Analysis of Deviance Table
##
## Model: gaussian, link: identity
##
## Response: richness
##
## Terms added sequentially (first to last)
##
##
## Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL 45 575.91
## mean_temp 1 212.116 44 363.80 < 2.2e-16
## site 2 13.679 42 350.12 0.0004135
## year 1 101.722 41 248.40 < 2.2e-16
## mean_humidity 1 36.989 40 211.41 8.530e-11
## mean_temp:site 2 105.199 38 106.21 < 2.2e-16
## mean_temp:year 1 22.336 37 83.87 4.559e-07
## site:year 2 17.597 35 66.28 4.442e-05
## mean_temp:mean_humidity 1 1.362 34 64.91 0.2129439
## site:mean_humidity 2 6.395 32 58.52 0.0261978
## year:mean_humidity 1 0.241 31 58.28 0.6000577
## mean_temp:site:year 2 7.846 29 50.43 0.0114660
## mean_temp:site:mean_humidity 2 3.530 27 46.90 0.1339519
## mean_temp:year:mean_humidity 1 16.254 26 30.65 1.686e-05
## site:year:mean_humidity 1 8.757 25 21.89 0.0015875
## mean_temp:site:year:mean_humidity 1 0.821 24 21.07 0.3334469
##
## NULL
## mean_temp ***
## site ***
## year ***
## mean_humidity ***
## mean_temp:site ***
## mean_temp:year ***
## site:year ***
## mean_temp:mean_humidity
## site:mean_humidity *
## year:mean_humidity
## mean_temp:site:year *
## mean_temp:site:mean_humidity
## mean_temp:year:mean_humidity ***
## site:year:mean_humidity **
## mean_temp:site:year:mean_humidity
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(anova_results5)
## Df Sum Sq Mean Sq F value Pr(>F)
## mean_temp 1 212.12 212.12 242.238 2.27e-14 ***
## site 2 13.68 6.84 7.811 0.002316 **
## year 1 101.72 101.72 116.168 6.92e-11 ***
## mean_humidity 1 36.99 36.99 42.241 8.30e-07 ***
## mean_temp:site 2 105.20 52.60 60.069 2.83e-10 ***
## mean_temp:year 1 22.34 22.34 25.508 3.27e-05 ***
## site:year 2 17.60 8.80 10.048 0.000627 ***
## mean_temp:mean_humidity 1 1.36 1.36 1.555 0.223910
## site:mean_humidity 2 6.39 3.20 3.652 0.040617 *
## year:mean_humidity 1 0.24 0.24 0.276 0.604211
## mean_temp:site:year 2 7.85 3.92 4.480 0.021736 *
## mean_temp:site:mean_humidity 2 3.53 1.76 2.015 0.154342
## mean_temp:year:mean_humidity 1 16.25 16.25 18.563 0.000224 ***
## site:year:mean_humidity 1 8.76 8.76 10.000 0.004075 **
## Residuals 25 21.89 0.88
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
e6 <- emmeans(model5, pairwise~site*year)
## NOTE: Results may be misleading due to involvement in interactions
e6
## $emmeans
## site year emmean SE df lower.CL upper.CL
## Carrizo 2022 12.10 11.85 24 -12.360 36.56
## Cuyama 2022 1.88 2.80 24 -3.898 7.67
## Tecopa 2022 161.29 1069.41 24 -2045.856 2368.43
## Carrizo 2023 10.67 4.99 24 0.371 20.97
## Cuyama 2023 142.87 38.22 24 63.983 221.75
## Tecopa 2023 33.32 63.59 24 -97.914 164.55
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## Carrizo year2022 - Cuyama year2022 10.22 12.17 24 0.839 0.9570
## Carrizo year2022 - Tecopa year2022 -149.19 1058.93 24 -0.141 1.0000
## Carrizo year2022 - Carrizo year2023 1.43 12.86 24 0.111 1.0000
## Carrizo year2022 - Cuyama year2023 -130.77 40.02 24 -3.268 0.0340
## Carrizo year2022 - Tecopa year2023 -21.22 64.68 24 -0.328 0.9994
## Cuyama year2022 - Tecopa year2022 -159.41 1069.41 24 -0.149 1.0000
## Cuyama year2022 - Carrizo year2023 -8.79 5.71 24 -1.538 0.6444
## Cuyama year2022 - Cuyama year2023 -140.98 38.32 24 -3.679 0.0133
## Cuyama year2022 - Tecopa year2023 -31.44 63.65 24 -0.494 0.9959
## Tecopa year2022 - Carrizo year2023 150.62 1069.27 24 0.141 1.0000
## Tecopa year2022 - Cuyama year2023 18.42 1070.09 24 0.017 1.0000
## Tecopa year2022 - Tecopa year2023 127.97 1098.55 24 0.116 1.0000
## Carrizo year2023 - Cuyama year2023 -132.19 38.54 24 -3.430 0.0236
## Carrizo year2023 - Tecopa year2023 -22.65 63.78 24 -0.355 0.9992
## Cuyama year2023 - Tecopa year2023 109.55 74.19 24 1.477 0.6816
##
## P value adjustment: tukey method for comparing a family of 6 estimates
### Evenness and temperature
model6 <- glm(Average_Evenness~mean_temp*site*year*mean_humidity, family = "gaussian", data = final_data)
model6
##
## Call: glm(formula = Average_Evenness ~ mean_temp * site * year * mean_humidity,
## family = "gaussian", data = final_data)
##
## Coefficients:
## (Intercept)
## -2.924e+04
## mean_temp
## 1.075e+03
## siteCuyama
## 1.978e+05
## siteTecopa
## 1.918e+04
## year
## 1.445e+01
## mean_humidity
## 1.089e+03
## mean_temp:siteCuyama
## -8.739e+03
## mean_temp:siteTecopa
## -7.814e+02
## mean_temp:year
## -5.312e-01
## siteCuyama:year
## -9.782e+01
## siteTecopa:year
## -9.486e+00
## mean_temp:mean_humidity
## -3.940e+01
## siteCuyama:mean_humidity
## -6.112e+03
## siteTecopa:mean_humidity
## -1.112e-02
## year:mean_humidity
## -5.383e-01
## mean_temp:siteCuyama:year
## 4.321e+00
## mean_temp:siteTecopa:year
## 3.864e-01
## mean_temp:siteCuyama:mean_humidity
## 2.686e+02
## mean_temp:siteTecopa:mean_humidity
## 5.490e-04
## mean_temp:year:mean_humidity
## 1.948e-02
## siteCuyama:year:mean_humidity
## 3.022e+00
## siteTecopa:year:mean_humidity
## NA
## mean_temp:siteCuyama:year:mean_humidity
## -1.328e-01
## mean_temp:siteTecopa:year:mean_humidity
## NA
##
## Degrees of Freedom: 45 Total (i.e. Null); 24 Residual
## Null Deviance: 0.3064
## Residual Deviance: 0.001288 AIC: -305.7
anova_results6 <- aov(model6, test = "Chisq")
## Warning: In lm.fit(x, y, offset = offset, singular.ok = singular.ok, ...) :
## extra argument 'test' will be disregarded
anova(model6, test = "Chisq")
## Analysis of Deviance Table
##
## Model: gaussian, link: identity
##
## Response: Average_Evenness
##
## Terms added sequentially (first to last)
##
##
## Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL 45 0.306353
## mean_temp 1 0.011046 44 0.295307 < 2.2e-16
## site 2 0.073313 42 0.221994 < 2.2e-16
## year 1 0.068416 41 0.153577 < 2.2e-16
## mean_humidity 1 0.004958 40 0.148620 < 2.2e-16
## mean_temp:site 2 0.112108 38 0.036512 < 2.2e-16
## mean_temp:year 1 0.002065 37 0.034447 5.578e-10
## site:year 2 0.012030 35 0.022417 < 2.2e-16
## mean_temp:mean_humidity 1 0.000145 34 0.022272 0.1001135
## site:mean_humidity 2 0.000774 32 0.021498 0.0007366
## year:mean_humidity 1 0.001450 31 0.020048 2.032e-07
## mean_temp:site:year 2 0.003008 29 0.017040 6.819e-13
## mean_temp:site:mean_humidity 2 0.005407 27 0.011633 < 2.2e-16
## mean_temp:year:mean_humidity 1 0.007064 26 0.004569 < 2.2e-16
## site:year:mean_humidity 1 0.002788 25 0.001781 5.712e-13
## mean_temp:site:year:mean_humidity 1 0.000492 24 0.001288 0.0024604
##
## NULL
## mean_temp ***
## site ***
## year ***
## mean_humidity ***
## mean_temp:site ***
## mean_temp:year ***
## site:year ***
## mean_temp:mean_humidity
## site:mean_humidity ***
## year:mean_humidity ***
## mean_temp:site:year ***
## mean_temp:site:mean_humidity ***
## mean_temp:year:mean_humidity ***
## site:year:mean_humidity ***
## mean_temp:site:year:mean_humidity **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(anova_results6)
## Df Sum Sq Mean Sq F value Pr(>F)
## mean_temp 1 0.01105 0.01105 155.089 3.23e-12 ***
## site 2 0.07331 0.03666 514.655 < 2e-16 ***
## year 1 0.06842 0.06842 960.555 < 2e-16 ***
## mean_humidity 1 0.00496 0.00496 69.605 1.08e-08 ***
## mean_temp:site 2 0.11211 0.05605 786.988 < 2e-16 ***
## mean_temp:year 1 0.00206 0.00206 28.990 1.38e-05 ***
## site:year 2 0.01203 0.00602 84.452 7.58e-12 ***
## mean_temp:mean_humidity 1 0.00015 0.00015 2.038 0.165804
## site:mean_humidity 2 0.00077 0.00039 5.437 0.010954 *
## year:mean_humidity 1 0.00145 0.00145 20.352 0.000132 ***
## mean_temp:site:year 2 0.00301 0.00150 21.114 4.26e-06 ***
## mean_temp:site:mean_humidity 2 0.00541 0.00270 37.958 2.66e-08 ***
## mean_temp:year:mean_humidity 1 0.00706 0.00706 99.178 3.48e-10 ***
## site:year:mean_humidity 1 0.00279 0.00279 39.150 1.52e-06 ***
## Residuals 25 0.00178 0.00007
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
e7 <- emmeans(model6, pairwise~site*year)
## NOTE: Results may be misleading due to involvement in interactions
e7
## $emmeans
## site year emmean SE df lower.CL upper.CL
## Carrizo 2022 0.2333 0.0927 24 0.0420 0.4246
## Cuyama 2022 -0.0603 0.0218 24 -0.1054 -0.0153
## Tecopa 2022 -0.0817 8.3624 24 -17.3409 17.1775
## Carrizo 2023 0.0399 0.0390 24 -0.0407 0.1204
## Cuyama 2023 2.0244 0.2989 24 1.4075 2.6413
## Tecopa 2023 -0.0993 0.4972 24 -1.1255 0.9269
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## Carrizo year2022 - Cuyama year2022 0.2937 0.0952 24 3.084 0.0508
## Carrizo year2022 - Tecopa year2022 0.3150 8.2805 24 0.038 1.0000
## Carrizo year2022 - Carrizo year2023 0.1935 0.1005 24 1.924 0.4125
## Carrizo year2022 - Cuyama year2023 -1.7911 0.3129 24 -5.724 0.0001
## Carrizo year2022 - Tecopa year2023 0.3326 0.5058 24 0.658 0.9849
## Cuyama year2022 - Tecopa year2022 0.0213 8.3625 24 0.003 1.0000
## Cuyama year2022 - Carrizo year2023 -0.1002 0.0447 24 -2.241 0.2565
## Cuyama year2022 - Cuyama year2023 -2.0848 0.2997 24 -6.957 <.0001
## Cuyama year2022 - Tecopa year2023 0.0390 0.4977 24 0.078 1.0000
## Tecopa year2022 - Carrizo year2023 -0.1215 8.3614 24 -0.015 1.0000
## Tecopa year2022 - Cuyama year2023 -2.1061 8.3678 24 -0.252 0.9998
## Tecopa year2022 - Tecopa year2023 0.0176 8.5903 24 0.002 1.0000
## Carrizo year2023 - Cuyama year2023 -1.9846 0.3014 24 -6.584 <.0001
## Carrizo year2023 - Tecopa year2023 0.1392 0.4987 24 0.279 0.9997
## Cuyama year2023 - Tecopa year2023 2.1237 0.5801 24 3.661 0.0139
##
## P value adjustment: tukey method for comparing a family of 6 estimates
### Ambient Temperature Plots
### Plots of temp might look super ugly.
abundance_temp <- ggplot(final_data, aes(mean_temp, animals)) +
geom_point(size = 0.5) +
facet_wrap(~year, scales = "free")+
scale_color_brewer(palette = "Set1") +
labs(x = "Average Temperature (C)", y = "Animal Abundance") + theme_classic() + theme(text = element_text(size = 12), panel.border = element_rect(color = "black", fill = NA, size = 1.5),legend.position = "none",legend.title = element_blank(), axis.text = element_text(size = 10)) +
geom_smooth(method = lm, se = TRUE) + theme(axis.title.x = element_blank()) + labs(tag = "A")
abundance_temp
## `geom_smooth()` using formula = 'y ~ x'
richness_temp <- ggplot(final_data, aes(mean_temp, richness)) +
geom_point(size = 0.5) +
facet_wrap(~year, scales = "free")+
scale_color_brewer(palette = "Set1") + theme(axis.title.x = element_blank()) + labs(tag = "B")+ theme_classic() + theme(text = element_text(size = 12), panel.border = element_rect(color = "black", fill = NA, size = 1.5),legend.position = "none",legend.title = element_blank(), axis.text = element_text(size = 10)) +
geom_smooth(method = lm, se = TRUE) +
labs(x = "Average Temperature (C)", y = "Richness") + theme(axis.title.x = element_blank())
richness_temp
## `geom_smooth()` using formula = 'y ~ x'
evenness_temp <- ggplot(final_data, aes(mean_temp, Average_Evenness)) +
geom_point(size = 0.5) +
facet_wrap(~year, scales = "free")+
scale_color_brewer(palette = "Set1") + theme_classic() + theme(legend.position = "none") + theme(text = element_text(size = 12), panel.border = element_rect(color = "black", fill = NA, size = 1.5),legend.position = "none", legend.title = element_blank(), axis.text = element_text(size = 10)) +
geom_smooth(method = lm, se = TRUE) + labs(tag = "C")+
labs(x = expression("Mean Temperature (°C)"), y = "Mean Evenness")
evenness_temp
## `geom_smooth()` using formula = 'y ~ x'
temp_plot <- abundance_temp/richness_temp/evenness_temp
temp_plot
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
### Other posisble figure options
abundance_2 <- ggplot(final_data, aes(shrub_density, animals)) + geom_point(size = 0.5) +
facet_wrap(~year, scales = "free") + scale_color_brewer(palette = "Set1") + theme_classic() + theme(legend.position = "none") + theme(text = element_text(size = 12), panel.border = element_rect(color = "black", fill = NA, size = 1.5),legend.position = "none", legend.title = element_blank(), axis.text = element_text(size = 10)) +
geom_smooth(method = lm, se = TRUE) + theme(axis.title.x = element_blank()) + labs(tag = "A")+
labs(x = "Shrub Density (Individuals per 20m radius)", y = "Animal Abundance")
abundance_2
## `geom_smooth()` using formula = 'y ~ x'
richness_2 <- ggplot(final_data, aes(shrub_density, richness)) + geom_point(size = 0.5) +
facet_wrap(~year, scales = "free") + scale_color_brewer(palette = "Set1") + theme_classic() + theme(legend.position = "none") + theme(text = element_text(size = 12), panel.border = element_rect(color = "black", fill = NA, size = 1.5),legend.position = "none", legend.title = element_blank(), axis.text = element_text(size = 10)) +
geom_smooth(method = lm, se = TRUE) + theme(axis.title.x = element_blank()) + labs(tag = "B")+
labs(x = "Shrub Density (Individuals per 20m radius)", y = "Richness")
richness_2
## `geom_smooth()` using formula = 'y ~ x'
evenness_2 <- ggplot(final_data, aes(shrub_density, Average_Evenness)) + geom_point(size = 0.5) +
facet_wrap(~year, scales = "free") + scale_color_brewer(palette = "Set1") + theme_classic() + theme(legend.position = "none") + theme(text = element_text(size = 12), panel.border = element_rect(color = "black", fill = NA, size = 1.5),legend.position = "none", legend.title = element_blank(), axis.text = element_text(size = 10)) +
geom_smooth(method = lm, se = TRUE) + labs(tag = "C")+
labs(x = expression("Shrub Density per " * 20 * m^2), y = "Mean Evenness")
plot1.1 <- abundance_2/richness_2/evenness_2
plot1.1
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
### Percent proportion Figure
#write.csv(density_obvs_final, file = "Animal Observations.csv")
scientific_names <- read.csv("Animal Observations.csv")
plot3 <- ggplot(scientific_names, aes(scientific_name, percent_presence, fill = microsite)) + geom_bar(stat = "identity") + coord_flip() + theme_classic() + scale_x_discrete(limits=rev) + xlab("Species") + ylab("Percent Proportion") + labs(fill = "Microsite")
plot3 <-plot3 + scale_fill_manual(values = c("#009900", "#0066cc"))
# Assuming you have a dataset named scientific_names with columns: scientific_name, percent_presence, microsite
# Create a new variable to specify the ordering based on presence in density or open areas
scientific_names$microsite <- ifelse(scientific_names$microsite == "Density", "Shrub", scientific_names$microsite)
scientific_names <- scientific_names %>%
mutate(ordering_var = ifelse(microsite == "Shrub", 1, 2)) %>%
arrange(ordering_var, desc(percent_presence))
# Create the histogram-style figure with reordering
plot3.2 <- ggplot(scientific_names, aes(fct_inorder(scientific_name), percent_presence, fill = microsite)) +
geom_bar(stat = "identity") +
coord_flip() +
theme_classic() +
xlab("Species") +
ylab("Percent Proportion") +
labs(fill = "Microsite") +
scale_fill_manual(values = c("#0066cc", "#009900")) + theme(text = element_text(size = 12), panel.border = element_rect(color = "black", fill = NA, size = 1.5), axis.text = element_text(size = 10))
# Reorder the x-axis labels
plot3.2 <- plot3.2 + scale_x_discrete(limits = rev(levels(scientific_names$scientific_name)))
# Plot the figure
print(plot3.2)
### Figure 1: Abundance, Richness, Evenness vs Shrub Density
plot1.1
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
### Figure 2: Abundance, Richness, Evenness vs Average Temperature
temp_plot
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
### Figure 3: PCOA of Communities
pcoa_final
### Figure 4: Percent Proportion of Vertebrate species
plot3.2
#write.csv(final_data, file = "Final Data.csv")
### Test figure with formula (Shrub Density)
final_data <- final_data %>%
mutate(year = as.character(year))
### Density v abundance
ggplot(final_data, aes(shrub_density, animals, color = year)) +
geom_smooth(method = "lm", formula = y ~ poly(x, 2), se = TRUE) +
scale_color_brewer(palette = "Set1") + labs(x = "Shrub Density 20m Radius", y = "Animal Abundance") + theme_classic() + theme(text = element_text(size = 12), panel.border = element_rect(color = "black", fill = NA, size = 1.5), axis.text = element_text(size = 10)) +
theme(axis.title.x = element_blank()) + labs(tag = "A")
### Density v Richness
ggplot(final_data, aes(shrub_density, richness, color = year)) +
geom_smooth(method = "lm", formula = y ~ poly(x, 2), se = TRUE) +
scale_color_brewer(palette = "Set1") + labs(x = "Shrub Density 20m Radius", y = "Richness") + theme_classic() + theme(text = element_text(size = 12), panel.border = element_rect(color = "black", fill = NA, size = 1.5), axis.text = element_text(size = 10)) +
theme(axis.title.x = element_blank()) + labs(tag = "B")
### Density v Evenness
ggplot(final_data, aes(shrub_density, Average_Evenness, color = year)) +
geom_smooth(method = "lm", formula = y ~ poly(x, 2), se = TRUE) +
scale_color_brewer(palette = "Set1") + labs(x = "Shrub Density 20m Radius", y = "Evenness") + theme_classic() + theme(text = element_text(size = 12), panel.border = element_rect(color = "black", fill = NA, size = 1.5), axis.text = element_text(size = 10)) +
theme(axis.title.x = element_blank()) + labs(tag = "C")
### Aridity v Abundance
ggplot(final_data, aes(aridity, animals, color = year)) +
geom_smooth(method = "lm", formula = y ~ poly(x, 2), se = TRUE) +
scale_color_brewer(palette = "Set1") + labs(x = "Aridity", y = "Animal Abundance") + theme_classic() + theme(text = element_text(size = 12), panel.border = element_rect(color = "black", fill = NA, size = 1.5), axis.text = element_text(size = 10)) +
theme(axis.title.x = element_blank()) + labs(tag = "A")
### Aridity v Richness
ggplot(final_data, aes(aridity, richness, color = year)) +
geom_smooth(method = "lm", formula = y ~ poly(x, 2), se = TRUE) +
scale_color_brewer(palette = "Set1") + labs(x = "Aridity", y = "Richness") + theme_classic() + theme(text = element_text(size = 12), panel.border = element_rect(color = "black", fill = NA, size = 1.5), axis.text = element_text(size = 10)) +
theme(axis.title.x = element_blank()) + labs(tag = "B")
### Aridity v Evenness
ggplot(final_data, aes(aridity, Average_Evenness, color = year)) +
geom_smooth(method = "lm", formula = y ~ poly(x, 2), se = TRUE) +
scale_color_brewer(palette = "Set1") + labs(x = "Aridity", y = "Evenness") + theme_classic() + theme(text = element_text(size = 12), panel.border = element_rect(color = "black", fill = NA, size = 1.5), axis.text = element_text(size = 10)) +
theme(axis.title.x = element_blank()) + labs(tag = "C")
### Stats
#install.packages("lme4", type = "source")
library(lmerTest)
## Loading required package: lme4
## Loading required package: Matrix
##
## Attaching package: 'Matrix'
## The following objects are masked from 'package:tidyr':
##
## expand, pack, unpack
##
## Attaching package: 'lmerTest'
## The following object is masked from 'package:lme4':
##
## lmer
## The following object is masked from 'package:stats':
##
## step
library(performance)
#simple
m1 <- lmer(animals ~ shrub_density + (1|year) + aridity, data = final_data)
check_collinearity(m1)
## # Check for Multicollinearity
##
## Low Correlation
##
## Term VIF VIF 95% CI Increased SE Tolerance Tolerance 95% CI
## shrub_density 1.01 [1.00, 2.65e+12] 1.00 0.99 [0.00, 1.00]
## aridity 1.01 [1.00, 2.65e+12] 1.00 0.99 [0.00, 1.00]
anova(m1)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## shrub_density 14370 14370 1 42.018 1.2164 0.27635
## aridity 114226 114226 1 42.010 9.6692 0.00336 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
lsmeansLT(m1)
## Least Squares Means table:
##
## Estimate Std. Error df t value lower upper Pr(>|t|)
##
## Confidence level: 95%
## Degrees of freedom method: Satterthwaite
ranova(m1) # I think this is to test for random effects across year?
## ANOVA-like table for random-effects: Single term deletions
##
## Model:
## animals ~ shrub_density + aridity + (1 | year)
## npar logLik AIC LRT Df Pr(>Chisq)
## <none> 5 -271.70 553.40
## (1 | year) 4 -274.97 557.94 6.5368 1 0.01057 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
m2 <- lmer(richness ~ shrub_density + (1|year) + aridity, data = final_data)
## boundary (singular) fit: see help('isSingular')
check_collinearity(m2)
## # Check for Multicollinearity
##
## Low Correlation
##
## Term VIF VIF 95% CI Increased SE Tolerance Tolerance 95% CI
## shrub_density 1.01 [1.00, 7.65e+12] 1.00 0.99 [0.00, 1.00]
## aridity 1.01 [1.00, 7.65e+12] 1.00 0.99 [0.00, 1.00]
anova(m2)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## shrub_density 17.096 17.096 1 43 1.8397 0.1820664
## aridity 149.184 149.184 1 43 16.0534 0.0002401 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
ranova(m2)
## ANOVA-like table for random-effects: Single term deletions
##
## Model:
## richness ~ shrub_density + aridity + (1 | year)
## npar logLik AIC LRT Df Pr(>Chisq)
## <none> 5 -116.83 243.66
## (1 | year) 4 -116.83 241.66 -8.5265e-14 1 1
m3 <- lmer(Average_Evenness ~ shrub_density + (1|year) + aridity, data = final_data)
## boundary (singular) fit: see help('isSingular')
check_collinearity(m3)
## # Check for Multicollinearity
##
## Low Correlation
##
## Term VIF VIF 95% CI Increased SE Tolerance Tolerance 95% CI
## shrub_density 1.01 [1.00, 7.65e+12] 1.00 0.99 [0.00, 1.00]
## aridity 1.01 [1.00, 7.65e+12] 1.00 0.99 [0.00, 1.00]
anova(m3)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## shrub_density 0.0215532 0.0215532 1 43 3.2712 0.0775 .
## aridity 0.0026079 0.0026079 1 43 0.3958 0.5326
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
ranova(m3)
## ANOVA-like table for random-effects: Single term deletions
##
## Model:
## Average_Evenness ~ shrub_density + aridity + (1 | year)
## npar logLik AIC LRT Df Pr(>Chisq)
## <none> 5 39.082 -68.164
## (1 | year) 4 39.082 -70.164 -1.4211e-14 1 1
trophic <- read.csv("Animal Observations.csv")
ggplot(trophic, aes(microsite, captures, color = Trophic)) +
geom_boxplot() +
scale_color_brewer(palette = "Set1") + labs(x = "Aridity", y = "Abundance") + theme_classic() + theme(text = element_text(size = 12), panel.border = element_rect(color = "black", fill = NA, size = 1.5), axis.text = element_text(size = 10)) +
theme(axis.title.x = element_blank()) + labs(tag = "C")
m9<- glm(total ~ microsite * Trophic, family = "poisson", data = trophic)
anova(m9, test = "Chisq")
## Analysis of Deviance Table
##
## Model: poisson, link: log
##
## Response: total
##
## Terms added sequentially (first to last)
##
##
## Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL 55 27821
## microsite 1 29.6 54 27791 5.449e-08 ***
## Trophic 2 7884.0 52 19907 < 2.2e-16 ***
## microsite:Trophic 2 2.0 50 19905 0.376
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
e9 <- emmeans(m9, pairwise~microsite|Trophic)
e9
## $emmeans
## Trophic = Carnivore:
## microsite emmean SE df asymp.LCL asymp.UCL
## Density 3.06 0.0767 Inf 2.91 3.21
## Open 3.18 0.0769 Inf 3.03 3.33
##
## Trophic = Herbivore:
## microsite emmean SE df asymp.LCL asymp.UCL
## Density 5.62 0.0173 Inf 5.59 5.66
## Open 5.71 0.0174 Inf 5.68 5.74
##
## Trophic = Omnivore:
## microsite emmean SE df asymp.LCL asymp.UCL
## Density 3.06 0.0685 Inf 2.92 3.19
## Open 3.00 0.0788 Inf 2.85 3.16
##
## Results are given on the log (not the response) scale.
## Confidence level used: 0.95
##
## $contrasts
## Trophic = Carnivore:
## contrast estimate SE df z.ratio p.value
## Density - Open -0.1276 0.1086 Inf -1.175 0.2400
##
## Trophic = Herbivore:
## contrast estimate SE df z.ratio p.value
## Density - Open -0.0849 0.0245 Inf -3.461 0.0005
##
## Trophic = Omnivore:
## contrast estimate SE df z.ratio p.value
## Density - Open 0.0567 0.1044 Inf 0.543 0.5869
##
## Results are given on the log (not the response) scale.
final_data_2022 <- final_data %>%
filter(year == "2022")
shapiro.test(final_data_2022$animals)
##
## Shapiro-Wilk normality test
##
## data: final_data_2022$animals
## W = 0.76017, p-value = 7.037e-05
ggqqplot(final_data_2022$animals)
n1 <- glm(animals ~ shrub_density * aridity, family = "gaussian", data = final_data_2022)
n1
##
## Call: glm(formula = animals ~ shrub_density * aridity, family = "gaussian",
## data = final_data_2022)
##
## Coefficients:
## (Intercept) shrub_density aridity
## -27.7891 0.6522 54.0811
## shrub_density:aridity
## 1.6648
##
## Degrees of Freedom: 23 Total (i.e. Null); 20 Residual
## Null Deviance: 619900
## Residual Deviance: 376700 AIC: 310
anova(n1, test = "Chisq")
## Analysis of Deviance Table
##
## Model: gaussian, link: identity
##
## Response: animals
##
## Terms added sequentially (first to last)
##
##
## Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL 23 619927
## shrub_density 1 26978 22 592949 0.2314117
## aridity 1 211738 21 381211 0.0008004 ***
## shrub_density:aridity 1 4465 20 376746 0.6263640
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
cat("Linear Model AIC:", AIC(n1), "\n")
## Linear Model AIC: 309.9796
quadratic_model_1 <- glm(animals ~ shrub_density + I(shrub_density^2) + aridity + I(aridity^2) + shrub_density:aridity, family = "gaussian", data = final_data_2022)
quadratic_model_1
##
## Call: glm(formula = animals ~ shrub_density + I(shrub_density^2) +
## aridity + I(aridity^2) + shrub_density:aridity, family = "gaussian",
## data = final_data_2022)
##
## Coefficients:
## (Intercept) shrub_density I(shrub_density^2)
## 139.405 100.932 -9.768
## aridity I(aridity^2) shrub_density:aridity
## -412.549 117.917 6.412
##
## Degrees of Freedom: 23 Total (i.e. Null); 18 Residual
## Null Deviance: 619900
## Residual Deviance: 186500 AIC: 297.1
anova(quadratic_model_1, test = "Chisq")
## Analysis of Deviance Table
##
## Model: gaussian, link: identity
##
## Response: animals
##
## Terms added sequentially (first to last)
##
##
## Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL 23 619927
## shrub_density 1 26978 22 592949 0.106605
## I(shrub_density^2) 1 2942 21 590007 0.594141
## aridity 1 261283 20 328724 5.12e-07 ***
## I(aridity^2) 1 89956 19 238768 0.003213 **
## shrub_density:aridity 1 52272 18 186496 0.024696 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
cat("Quadratic Model AIC:", AIC(quadratic_model_1), "\n")
## Quadratic Model AIC: 297.1038
if (AIC(quadratic_model_1) < AIC(n1)) {
cat("Quadratic model is better.\n")
} else {
cat("Linear model is better or equally good.\n")
}
## Quadratic model is better.
abund_dens_2022 <- ggplot(final_data_2022, aes(shrub_density, animals)) +
geom_smooth(method = "lm", formula = y ~ poly(x, 2), se = TRUE, color = "black") +
scale_color_brewer(palette = "Set1") + labs(x = "Shrub Density 20m Radius", y = "Animal Abundance") + theme_classic() + theme(text = element_text(size = 12), panel.border = element_rect(color = "black", fill = NA, size = 1.5), axis.text = element_text(size = 10)) +
theme(axis.title.x = element_blank()) + labs(tag = "A") + theme(text = element_text(size = 12), panel.border = element_rect(color = "black", fill = NA, size = 1.5), axis.text = element_text(size = 10)) + theme(aspect.ratio = 0.7) + theme(legend.position = "none") + theme(legend.text = element_text(size = 8))
### Richness
n2 <- glm(richness ~ shrub_density * aridity, family = "gaussian", data = final_data_2022)
n2
##
## Call: glm(formula = richness ~ shrub_density * aridity, family = "gaussian",
## data = final_data_2022)
##
## Coefficients:
## (Intercept) shrub_density aridity
## -0.34946 0.08662 2.58054
## shrub_density:aridity
## -0.01174
##
## Degrees of Freedom: 23 Total (i.e. Null); 20 Residual
## Null Deviance: 475
## Residual Deviance: 132.4 AIC: 119.1
anova(n2, test = "Chisq")
## Analysis of Deviance Table
##
## Model: gaussian, link: identity
##
## Response: richness
##
## Terms added sequentially (first to last)
##
##
## Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL 23 474.96
## shrub_density 1 6.43 22 468.53 0.3243
## aridity 1 335.94 21 132.58 1.043e-12 ***
## shrub_density:aridity 1 0.22 20 132.36 0.8546
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
cat("Linear Model AIC:", AIC(n2), "\n")
## Linear Model AIC: 119.0888
quadratic_model_2 <- glm(richness ~ shrub_density + I(shrub_density^2) + aridity + I(aridity^2) + shrub_density:aridity, family = "gaussian", data = final_data_2022)
quadratic_model_2
##
## Call: glm(formula = richness ~ shrub_density + I(shrub_density^2) +
## aridity + I(aridity^2) + shrub_density:aridity, family = "gaussian",
## data = final_data_2022)
##
## Coefficients:
## (Intercept) shrub_density I(shrub_density^2)
## 1.9865 -0.7054 0.0776
## aridity I(aridity^2) shrub_density:aridity
## -3.8810 1.6566 -0.0559
##
## Degrees of Freedom: 23 Total (i.e. Null); 18 Residual
## Null Deviance: 475
## Residual Deviance: 87.2 AIC: 113.1
anova(quadratic_model_2, test = "Chisq")
## Analysis of Deviance Table
##
## Model: gaussian, link: identity
##
## Response: richness
##
## Terms added sequentially (first to last)
##
##
## Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL 23 474.96
## shrub_density 1 6.431 22 468.53 0.24925
## I(shrub_density^2) 1 106.430 21 362.10 2.769e-06 ***
## aridity 1 247.045 20 115.05 9.248e-13 ***
## I(aridity^2) 1 23.883 19 91.17 0.02639 *
## shrub_density:aridity 1 3.972 18 87.20 0.36517
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
cat("Quadratic Model AIC:", AIC(quadratic_model_2), "\n")
## Quadratic Model AIC: 113.0717
if (AIC(quadratic_model_2) < AIC(n2)) {
cat("Quadratic model is better.\n")
} else {
cat("Linear model is better or equally good.\n")
}
## Quadratic model is better.
rich_dens_2022 <- ggplot(final_data_2022, aes(shrub_density, richness)) +
geom_smooth(method = "lm", formula = y ~ poly(x, 2), se = TRUE, color = "black") +
scale_color_brewer(palette = "Set1") + labs(x = "Shrub Density 20m Radius", y = "Richness") + theme_classic() + theme(text = element_text(size = 12), panel.border = element_rect(color = "black", fill = NA, size = 1.5), axis.text = element_text(size = 10)) +
theme(axis.title.x = element_blank()) + labs(tag = "B") + theme(text = element_text(size = 12), panel.border = element_rect(color = "black", fill = NA, size = 1.5), axis.text = element_text(size = 10)) + theme(aspect.ratio = 0.7) + theme(legend.position = "none") + theme(legend.text = element_text(size = 8))
### Evenness
n3 <- glm(Average_Evenness ~ shrub_density * aridity, data = final_data_2022)
n3
##
## Call: glm(formula = Average_Evenness ~ shrub_density * aridity, data = final_data_2022)
##
## Coefficients:
## (Intercept) shrub_density aridity
## 0.019618 -0.001800 0.030276
## shrub_density:aridity
## 0.001651
##
## Degrees of Freedom: 23 Total (i.e. Null); 20 Residual
## Null Deviance: 0.1455
## Residual Deviance: 0.05188 AIC: -69.18
anova(n3, test = "Chisq")
## Analysis of Deviance Table
##
## Model: gaussian, link: identity
##
## Response: Average_Evenness
##
## Terms added sequentially (first to last)
##
##
## Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL 23 0.145519
## shrub_density 1 0.007333 22 0.138186 0.0927 .
## aridity 1 0.081917 21 0.056269 1.913e-08 ***
## shrub_density:aridity 1 0.004391 20 0.051878 0.1932
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
cat("Linear Model AIC:", AIC(n3), "\n")
## Linear Model AIC: -69.17676
quadratic_model_3 <- glm(Average_Evenness ~ shrub_density + I(shrub_density^2) + aridity + I(aridity^2) + shrub_density:aridity, family = "gaussian", data = final_data_2022)
quadratic_model_3
##
## Call: glm(formula = Average_Evenness ~ shrub_density + I(shrub_density^2) +
## aridity + I(aridity^2) + shrub_density:aridity, family = "gaussian",
## data = final_data_2022)
##
## Coefficients:
## (Intercept) shrub_density I(shrub_density^2)
## 0.0446964 -0.0295177 0.0027085
## aridity I(aridity^2) shrub_density:aridity
## -0.0385879 0.0178654 0.0002091
##
## Degrees of Freedom: 23 Total (i.e. Null); 18 Residual
## Null Deviance: 0.1455
## Residual Deviance: 0.03225 AIC: -76.59
anova(quadratic_model_3, test = "Chisq")
## Analysis of Deviance Table
##
## Model: gaussian, link: identity
##
## Response: Average_Evenness
##
## Terms added sequentially (first to last)
##
##
## Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL 23 0.145519
## shrub_density 1 0.007333 22 0.138186 0.04307 *
## I(shrub_density^2) 1 0.056383 21 0.081804 2.027e-08 ***
## aridity 1 0.047100 20 0.034703 2.941e-07 ***
## I(aridity^2) 1 0.002397 19 0.032306 0.24743
## shrub_density:aridity 1 0.000056 18 0.032251 0.86020
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
cat("Quadratic Model AIC:", AIC(quadratic_model_2), "\n")
## Quadratic Model AIC: 113.0717
if (AIC(quadratic_model_2) < AIC(n2)) {
cat("Quadratic model is better.\n")
} else {
cat("Linear model is better or equally good.\n")
}
## Quadratic model is better.
even_dens_2022 <- ggplot(final_data_2022, aes(shrub_density, Average_Evenness)) +
geom_smooth(method = "lm", formula = y ~ poly(x, 2), se = TRUE, color = "black") +
scale_color_brewer(palette = "Set1") + labs(x = "Shrub Density per 20m Radius", y = "Evenness") + theme_classic() + theme(text = element_text(size = 12), panel.border = element_rect(color = "black", fill = NA, size = 1.5), axis.text = element_text(size = 10))+
labs(tag = "C") + theme(text = element_text(size = 12), panel.border = element_rect(color = "black", fill = NA, size = 1.5), axis.text = element_text(size = 10)) + theme(aspect.ratio = 0.7) + theme(legend.position = "none") + theme(legend.text = element_text(size = 8))
plot <- abund_dens_2022/rich_dens_2022/even_dens_2022
plot
### Temperature (Hold off on this and ask chris)
t1 <- glm(animals ~ shrub_density * mean_temp, family = "gaussian", data = final_data_2022)
t1
##
## Call: glm(formula = animals ~ shrub_density * mean_temp, family = "gaussian",
## data = final_data_2022)
##
## Coefficients:
## (Intercept) shrub_density mean_temp
## 641.3669 22.3664 -20.8447
## shrub_density:mean_temp
## -0.6221
##
## Degrees of Freedom: 23 Total (i.e. Null); 20 Residual
## Null Deviance: 619900
## Residual Deviance: 389100 AIC: 310.8
anova(t1, test = "Chisq")
## Analysis of Deviance Table
##
## Model: gaussian, link: identity
##
## Response: animals
##
## Terms added sequentially (first to last)
##
##
## Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL 23 619927
## shrub_density 1 26978 22 592949 0.238985
## mean_temp 1 199846 21 393103 0.001351 **
## shrub_density:mean_temp 1 3970 20 389134 0.651496
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
cat("Linear Model AIC:", AIC(t1), "\n")
## Linear Model AIC: 310.756
quadratic_temp_1 <- glm(animals ~ shrub_density + I(shrub_density^2) + mean_temp + I(mean_temp^2) + shrub_density:mean_temp, family = "gaussian", data = final_data_2022)
quadratic_temp_1
##
## Call: glm(formula = animals ~ shrub_density + I(shrub_density^2) +
## mean_temp + I(mean_temp^2) + shrub_density:mean_temp, family = "gaussian",
## data = final_data_2022)
##
## Coefficients:
## (Intercept) shrub_density I(shrub_density^2)
## 4363.574 155.190 -6.801
## mean_temp I(mean_temp^2) shrub_density:mean_temp
## -305.916 5.317 -2.618
##
## Degrees of Freedom: 23 Total (i.e. Null); 18 Residual
## Null Deviance: 619900
## Residual Deviance: 301200 AIC: 308.6
anova(quadratic_temp_1, test = "Chisq")
## Analysis of Deviance Table
##
## Model: gaussian, link: identity
##
## Response: animals
##
## Terms added sequentially (first to last)
##
##
## Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL 23 619927
## shrub_density 1 26978 22 592949 0.2042037
## I(shrub_density^2) 1 2942 21 590007 0.6750263
## mean_temp 1 240180 20 349827 0.0001516 ***
## I(mean_temp^2) 1 2772 19 347055 0.6840117
## shrub_density:mean_temp 1 45820 18 301235 0.0979909 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
cat("Quadratic Model AIC:", AIC(quadratic_temp_1), "\n")
## Quadratic Model AIC: 308.6113
final_data_2023 <- final_data %>%
filter(year == "2023")
x1 <- glm(animals ~ shrub_density * aridity, family = "gaussian", data = final_data_2023)
x1
##
## Call: glm(formula = animals ~ shrub_density * aridity, family = "gaussian",
## data = final_data_2023)
##
## Coefficients:
## (Intercept) shrub_density aridity
## 8.32431 1.85904 2.30955
## shrub_density:aridity
## -0.05488
##
## Degrees of Freedom: 21 Total (i.e. Null); 18 Residual
## Null Deviance: 12650
## Residual Deviance: 10190 AIC: 207.5
anova(x1, test = "Chisq")
## Analysis of Deviance Table
##
## Model: gaussian, link: identity
##
## Response: animals
##
## Terms added sequentially (first to last)
##
##
## Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL 21 12649
## shrub_density 1 2257.72 20 10391 0.04579 *
## aridity 1 199.04 19 10192 0.55317
## shrub_density:aridity 1 4.64 18 10187 0.92782
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
cat("Linear Model AIC:", AIC(x1), "\n")
## Linear Model AIC: 207.4663
quadratic_model_x1 <- glm(animals ~ shrub_density + I(shrub_density^2) + aridity + I(aridity^2) + shrub_density:aridity, family = "gaussian", data = final_data_2023)
quadratic_model_x1
##
## Call: glm(formula = animals ~ shrub_density + I(shrub_density^2) +
## aridity + I(aridity^2) + shrub_density:aridity, family = "gaussian",
## data = final_data_2023)
##
## Coefficients:
## (Intercept) shrub_density I(shrub_density^2)
## -1.65760 3.43942 -0.15541
## aridity I(aridity^2) shrub_density:aridity
## 29.97243 -7.06338 0.03991
##
## Degrees of Freedom: 21 Total (i.e. Null); 16 Residual
## Null Deviance: 12650
## Residual Deviance: 9619 AIC: 210.2
anova(quadratic_model_x1, test = "Chisq")
## Analysis of Deviance Table
##
## Model: gaussian, link: identity
##
## Response: animals
##
## Terms added sequentially (first to last)
##
##
## Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL 21 12648.8
## shrub_density 1 2257.72 20 10391.1 0.05264 .
## I(shrub_density^2) 1 55.46 19 10335.6 0.76135
## aridity 1 317.46 18 10018.1 0.46743
## I(aridity^2) 1 396.75 17 9621.4 0.41659
## shrub_density:aridity 1 1.93 16 9619.5 0.95482
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
cat("Quadratic Model AIC:", AIC(quadratic_model_x1), "\n")
## Quadratic Model AIC: 210.2043
if (AIC(quadratic_model_x1) < AIC(x1)) {
cat("Quadratic model is better.\n")
} else {
cat("Linear model is better or equally good.\n")
}
## Linear model is better or equally good.
abund_dens_2023 <- ggplot(final_data_2023, aes(shrub_density, animals)) +
geom_smooth(method = "lm", se = TRUE, color = "black") +
scale_color_brewer(palette = "Set1") + labs(x = "Shrub Density 20m Radius", y = "Animal Abundance") + theme_classic() + theme(text = element_text(size = 12), panel.border = element_rect(color = "black", fill = NA, size = 1.5), axis.text = element_text(size = 10)) +
theme(axis.title.x = element_blank()) + labs(tag = "A") + theme(text = element_text(size = 12), panel.border = element_rect(color = "black", fill = NA, size = 1.5), axis.text = element_text(size = 10)) + theme(aspect.ratio = 0.7) + theme(legend.position = "none") + theme(legend.text = element_text(size = 8))
abund_dens_2023
## `geom_smooth()` using formula = 'y ~ x'
x2 <- glm(richness ~ shrub_density * aridity, data = final_data_2023)
x2
##
## Call: glm(formula = richness ~ shrub_density * aridity, data = final_data_2023)
##
## Coefficients:
## (Intercept) shrub_density aridity
## 4.775057 0.220144 -0.167510
## shrub_density:aridity
## -0.009058
##
## Degrees of Freedom: 21 Total (i.e. Null); 18 Residual
## Null Deviance: 95.32
## Residual Deviance: 67.28 AIC: 97.03
anova(x2, test = "Chisq")
## Analysis of Deviance Table
##
## Model: gaussian, link: identity
##
## Response: richness
##
## Terms added sequentially (first to last)
##
##
## Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL 21 95.318
## shrub_density 1 25.4984 20 69.820 0.009006 **
## aridity 1 2.4110 19 67.409 0.421898
## shrub_density:aridity 1 0.1265 18 67.282 0.854019
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
cat("Linear Model AIC:", AIC(x2), "\n")
## Linear Model AIC: 97.02606
quadratic_model_x2 <- glm(richness ~ shrub_density + I(shrub_density^2) + aridity + I(aridity^2) + shrub_density:aridity, data = final_data_2023)
quadratic_model_x2
##
## Call: glm(formula = richness ~ shrub_density + I(shrub_density^2) +
## aridity + I(aridity^2) + shrub_density:aridity, data = final_data_2023)
##
## Coefficients:
## (Intercept) shrub_density I(shrub_density^2)
## 3.32139 -0.05358 0.02657
## aridity I(aridity^2) shrub_density:aridity
## 3.87515 -1.02486 -0.02127
##
## Degrees of Freedom: 21 Total (i.e. Null); 16 Residual
## Null Deviance: 95.32
## Residual Deviance: 59.25 AIC: 98.23
anova(quadratic_model_x2, test = "Chisq")
## Analysis of Deviance Table
##
## Model: gaussian, link: identity
##
## Response: richness
##
## Terms added sequentially (first to last)
##
##
## Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL 21 95.318
## shrub_density 1 25.4984 20 69.820 0.008689 **
## I(shrub_density^2) 1 0.4031 19 69.417 0.741448
## aridity 1 2.0180 18 67.399 0.460389
## I(aridity^2) 1 7.6014 17 59.797 0.151936
## shrub_density:aridity 1 0.5479 16 59.249 0.700486
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
cat("Quadratic Model AIC:", AIC(quadratic_model_x2), "\n")
## Quadratic Model AIC: 98.22897
if (AIC(quadratic_model_x2) < AIC(x2)) {
cat("Quadratic model is better.\n")
} else {
cat("Linear model is better or equally good.\n")
}
## Linear model is better or equally good.
rich_dens_2023 <- ggplot(final_data_2023, aes(shrub_density, richness)) +
geom_smooth(method = "lm", se = TRUE, color = "black") +
scale_color_brewer(palette = "Set1") + labs(x = "Shrub Density 20m Radius", y = "Richness") + theme_classic() + theme(text = element_text(size = 12), panel.border = element_rect(color = "black", fill = NA, size = 1.5), axis.text = element_text(size = 10)) +
theme(axis.title.x = element_blank()) + labs(tag = "B") + theme(text = element_text(size = 12), panel.border = element_rect(color = "black", fill = NA, size = 1.5), axis.text = element_text(size = 10)) + theme(aspect.ratio = 0.7) + theme(legend.position = "none") + theme(legend.text = element_text(size = 8))
rich_dens_2023
## `geom_smooth()` using formula = 'y ~ x'
x3 <- glm(Average_Evenness ~ shrub_density * aridity, data = final_data_2023)
x3
##
## Call: glm(formula = Average_Evenness ~ shrub_density * aridity, data = final_data_2023)
##
## Coefficients:
## (Intercept) shrub_density aridity
## 0.1816823 0.0084948 -0.0469045
## shrub_density:aridity
## -0.0009284
##
## Degrees of Freedom: 21 Total (i.e. Null); 18 Residual
## Null Deviance: 0.1608
## Residual Deviance: 0.009233 AIC: -98.64
anova(x3, test = "Chisq")
## Analysis of Deviance Table
##
## Model: gaussian, link: identity
##
## Response: Average_Evenness
##
## Terms added sequentially (first to last)
##
##
## Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL 21 0.160797
## shrub_density 1 0.013715 20 0.147082 2.33e-07 ***
## aridity 1 0.136519 19 0.010562 < 2.2e-16 ***
## shrub_density:aridity 1 0.001329 18 0.009233 0.1074
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
cat("Linear Model AIC:", AIC(x3), "\n")
## Linear Model AIC: -98.63904
quadratic_model_x3 <- glm(Average_Evenness ~ shrub_density + I(shrub_density^2) + aridity + I(aridity^2) + shrub_density:aridity, data = final_data_2023)
quadratic_model_x3
##
## Call: glm(formula = Average_Evenness ~ shrub_density + I(shrub_density^2) +
## aridity + I(aridity^2) + shrub_density:aridity, data = final_data_2023)
##
## Coefficients:
## (Intercept) shrub_density I(shrub_density^2)
## 0.1963905 0.0105327 -0.0001974
## aridity I(aridity^2) shrub_density:aridity
## -0.0877876 0.0103750 -0.0008426
##
## Degrees of Freedom: 21 Total (i.e. Null); 16 Residual
## Null Deviance: 0.1608
## Residual Deviance: 0.008405 AIC: -96.71
anova(quadratic_model_x3, test = "Chisq")
## Analysis of Deviance Table
##
## Model: gaussian, link: identity
##
## Response: Average_Evenness
##
## Terms added sequentially (first to last)
##
##
## Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL 21 0.160797
## shrub_density 1 0.013715 20 0.147082 3.226e-07 ***
## I(shrub_density^2) 1 0.020159 19 0.126922 5.833e-10 ***
## aridity 1 0.116561 18 0.010361 < 2.2e-16 ***
## I(aridity^2) 1 0.001096 17 0.009265 0.1485
## shrub_density:aridity 1 0.000860 16 0.008405 0.2006
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
cat("Quadratic Model AIC:", AIC(quadratic_model_x3), "\n")
## Quadratic Model AIC: -96.7066
if (AIC(quadratic_model_x1) < AIC(x1)) {
cat("Quadratic model is better.\n")
} else {
cat("Linear model is better or equally good.\n")
}
## Linear model is better or equally good.
even_dens_2023 <- ggplot(final_data_2023, aes(shrub_density, Average_Evenness)) +
geom_smooth(method = "lm", se = TRUE, color = "black") +
scale_color_brewer(palette = "Set1") + labs(x = "Shrub Density per 20m Radius", y = "Evenness") + theme_classic() + theme(text = element_text(size = 12), panel.border = element_rect(color = "black", fill = NA, size = 1.5), axis.text = element_text(size = 10))+
labs(tag = "C") + theme(text = element_text(size = 12), panel.border = element_rect(color = "black", fill = NA, size = 1.5), axis.text = element_text(size = 10)) + theme(aspect.ratio = 0.7) + theme(legend.position = "none") + theme(legend.text = element_text(size = 8))
even_dens_2023
## `geom_smooth()` using formula = 'y ~ x'
plot2 <- abund_dens_2023/rich_dens_2023/even_dens_2023
plot2
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'